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Conventional recommender systems of cold chain logistics distribution mainly focus on the recommendations of the source of cargos, refrigerator trucks and refrigerators in the supply and demand link of cold chain, but ignore contextual information such as time, position and user devices. In this paper, we analyze the contextual information on cold chain logistics distribution and propose a multidimensional context-aware recommendation algorithm(MCARA). MCARA firstly carries out fuzzy clustering on contextual information in historical data set and obtains the contextual clusters. In addition, MCARA compares current user context with historical contexts to get current contextual cluster, and selects out the data with same contextual clusters from historical data set. Finally, MCARA uses the user-based collaborative filtering algorithm to perform personalized recommendations. The simulation results show that MCARA can improve the forecast accuracy of cold chain logistics distribution, with about 10% improvement over other eight approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framework consists of a meta-controller, which receives the user utterance and understands which goal should pursue, and a controller, which exploits a goal-specific representation to generate an answer composed by a sequence of tokens. The modules are trained using a two-stage strategy based on a preliminary Supervised Learning stage and a successive Reinforcement Learning stage. [ABSTRACT FROM AUTHOR]

Web growth, especially in social networks, is continuously increasing every day. Multiplicity of products offered and web pages has made picking up relevant items a tedious job. On the other hand, different tastes and behaviors of users is creating the probability to find a similar user among a large group of users difficult. As a result, automated software systems have difficulty to discover what is interesting to users. We have proposed a new approach to adapt to this flow. We will exploit domain knowledge of training data set to create a summary matrix. The summary matrix consists of new and few columns according to the attribute values of the selected feature. We fill the summary matrix with the average ratings based on the number of times that the attribute values appear in the user's profile for rated items. We use the summary matrix in two hybrid recommender systems. In our approach, we use meta-level technique which is one of the pipelined hybridization techniques. The proposed approach will reduce the effects of sparsity, cold start, and scalability which are common problems with the collaborative recommender systems. Furthermore, the proposed approach will improve the recommendation accuracy when there is comparison with the Collaborative Filtering Pearson Correlation approach and it will be faster as well. [ABSTRACT FROM AUTHOR]

Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs – and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of recommendation with the context of one-class classification. Specifically, we employ the alternative machine learning framework of Artificial Immune Systems (AIS) in order to develop user-specific preference models. Our approach is based on the fact that users experience a major difficulty in articulating their music preferences while at the same time they are extremely reluctant in providing counter-examples of their music habits. Therefore, developing coherent user models on the grounds of both positive (desirable) and negative (non-desirable) training samples is not a feasible task since the class of non-favorable data patterns is severely under-represented. Our recommendation approach alleviates the need to collect negative feedback form the user by building recommendation models that exclusively rely on the presence of a limited number positive data items. Such models are built, however, by trying to efficiently cover the complementary space of non-desirable patterns. This is achieved through the utilization of V-Detector, an AIS-based one-class classification algorithm, which operates by developing a set of variable-sized detectors for the subspace of non-preferable music items. V-Detector, despite being exclusively fed with instances from the positive class, focuses on delivering an accurate model of the negative space. Based on this complementarity, our recommendation algorithm is able to implicitly model individual user preferences that span arbitrary-shaped and fragmented regions of the complete space of patterns. The proposed recommendation approach was experimentally evaluated in terms of its efficiency to correctly identify the user-defined classes of positive and negative preference. The obtained results justify its superiority against traditional one-class classification approaches. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) suffer from cold start and data sparsity problem. Researchers have proposed various solutions to this problem in which cross domain recommendation is an effective approach. Cross domain recommender system (CDRS) utilizes user data from multiple domains to generate prediction for the target user. This paper proposes a proactive cross domain recommender system. This paper also introduces a parallel approach in cross domain recommendation using general purpose graphic processing unit (GPGPU). This will help to accelerate the computation in the multi-agent environment as data processing in multiple domains takes significant amount of time. A prototype of the system is developed in tourism domain using Cuda, JCuda, Java, Android studio and Jade. The system uses four domains which is restaurant, tourist places, shopping places and hotels. The performance of the parallel CDRS system is compared with non-parallel CDRS in terms of their processing speed. Also the system is compared to the normal Collaborative Filtering approach to measure accuracy of the proposed system using MAE as well as precision, recall and F-measure. The results show a significant speedup for the presented system over non-parallel system. [ABSTRACT FROM AUTHOR]

With the popularity of online hotels booking, increasing attention has been paid to hotel recommendation methods. To provide personalized hotel recommendation for different types of consumers, a new hotel recommendation approach is proposed based on consumers' online reviews using interval neutrosophic linguistic numbers (INLNs). Meanwhile, this paper puts forward a distance formula of the interval neutrosophic linguistic numbers and the interval neutrosophic linguistic numbers power average (INLNPA) operator, making a further extension on the basis of the INLNs. Moreover, we develop a novel integration model utilizing the INLNPA operator that takes into consideration the relevance of the similar groups. And we apply the proposed approach to the hotel recommendation. In the case study, we have extracted 1902 online reviews of 10 hotels from TripAdvisor.com to verify the reliability of the proposed approach. The main conclusion of this paper is that the reliability of the hotel ordering can be improved by using the proposed approach. [ABSTRACT FROM AUTHOR]

Social Networks have experienced increased popularity and rapid growth in recent years. Recommendation is significant for users due to the extremely large amount of information in Social Networks. Most existing recommender systems rely on collaborative filtering techniques which focus on recommending the most relevant items to users based on past rating information of users or items. In Social Networks, the cold-start and data sparsity problems are very serious because new users and items are growing rapidly. Taking the Event Recommendation problem in Event-Based Social Networks as a scenario, many events are newly created and have few feedbacks. Existed collaborative filtering based methods will fail for Social Recommendation due to these problems. Therefore, a more sophisticated recommendation mechanism that can efficiently combine various contextual information to further improve recommendation quality is desired. In this paper, we propose a Context Aware Matrix Factorization model called CAMF which models implicit feedbacks and various contextual information simultaneously for Social Recommendation. Specifically, CAMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear Contextual Features model which models explicit contextual features. Extensive experiments on a large real-world dataset demonstrate that the CAMF model significantly outperforms state-of-the-art methods by 12.7% in terms of accuracy for the Event Recommendation problem. [ABSTRACT FROM AUTHOR]

Recommender systems aim to provide users with preferred items to address the information overload problem in the Web era. Social relations, item connections, and user-generated item reviews and ratings play important roles in recommender systems as they contain abundant potential information. Many methods have been proposed to predict users' ratings by learning latent topic factors from their reviews and ratings of corresponding items. However, these methods ignore the relationships among items and cannot make full use of the complicated relations between reviews and ratings. Motivated by this observation, we integrate ratings, reviews, user connections and item relations to improve recommendations by combining matrix factorization with the Latent Dirichlet Allocation (LDA) model. Experimental results on two real-world datasets prove that item–item relations contain useful information for recommendations, and our model effectively improves recommendation quality. [ABSTRACT FROM AUTHOR]

Recommender systems aim to provide users with preferred items to address the information overload problem in the Web era. Social relations, item connections, and user-generated item reviews and ratings play important roles in recommender systems as they contain abundant potential information. Many methods have been proposed to predict users' ratings by learning latent topic factors from their reviews and ratings of corresponding items. However, these methods ignore the relationships among items and cannot make full use of the complicated relations between reviews and ratings. Motivated by this observation, we integrate ratings, reviews, user connections and item relations to improve recommendations by combining matrix factorization with the Latent Dirichlet Allocation (LDA) model. Experimental results on two real-world datasets prove that item–item relations contain useful information for recommendations, and our model effectively improves recommendation quality. [ABSTRACT FROM AUTHOR]

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc. [ABSTRACT FROM AUTHOR]

Location-based social networks are becoming a unique platform for understanding user behaviors and providing pervasive services in intelligent environments. However, fake users or accounts can undermine user analytics and lower the value of the applications and services intended for real users. Mining a large Foursquare dataset and related Twitter accounts, we tested different user features with the goal of classifying fake users. Experiments demonstrate an accuracy over 95% in detecting fake users. Filtering out these fake users reduces the error rate of a location-based activity predictor by a 4.4% and avoids wasting 35% of coupons or promotion codes delivery if applied to a recommender system. [ABSTRACT FROM AUTHOR]

The number of motorcycles on the road has increased in almost all European countries according to Eurostat. Although the total number of motorcycles is lower than the number of cars, the accident rate is much higher. A large number of these accidents are due to human errors. Stress is one of the main reasons behind human errors while driving. In this paper, we present a novel mechanism to predict upcoming values for stress levels based on current and past values for both the driving behavior and environmental factors. First, we analyze the relationship between stress levels and different variables that model the driving behavior (accelerations, decelerations, positive kinetic energy, standard deviation of speed, and road shape). Stress levels are obtained utilizing a Polar H7 heart rate strap. Vehicle telemetry is captured using a smartphone. Second, we study the accuracy of several machine learning algorithms (Support Vector Machine, Multilayer Perceptron, Naïve Bayes, J48, and Deep Belief Network) when used to estimate the stress based on our input data. Finally, an experiment was conducted in a real environment. We considered three different scenarios: home-workplace route, workplace-home route, and driving under heavy traffic. The results show that the proposal can estimate the upcoming stress with high accuracy. This algorithm could be used to develop driving assistants that recommend actions to prevent the stress. [ABSTRACT FROM AUTHOR]

Proliferation of information is a major confront faced by e-commerce industry. To ease the customers from this information proliferation, Recommender Systems (RS) were introduced. To improve the computational time of a RS for large scale data, the process of recommendation can be implemented on a scalable, fault tolerant and a distributed processing framework. This paper proposes a Content-Based RS implemented on scalable, fault tolerant and distributed framework of Hadoop Map Reduce. To generate recommendations with improved computational time, the proposed technique of Map Reduce Content-Based Recommendation (MRCBR) is implemented using Hadoop Map Reduce which follows the traditional process of content-based recommendation. MRCBR technique comprises of user profiling and document feature extraction which uses the vector space model followed by computing similarity to generate recommendation for the target user. Recommendations generated for the target user is a set of Top N documents. The proposed technique of recommendation is executed on a cluster of Hadoop and is tested for News dataset. News items are collected using RSS feeds and are stored in MongoDB. Computational time of MRCBR is evaluated with a Speedup factor and performance is evaluated with the standard evaluation metric of Precision, Recall and F-Measure. [ABSTRACT FROM AUTHOR]

With the enormous growth in the volume of online data, users are flooded with a gigantic amount of information. This has made the task of Recommender systems (RSs) even more engrossing. Research in RSs has been revolving around newer concepts like social factors, context of the user and the groups they belong to. This paper presents the design and development of a Community based Collaborative Filtering Recommender System (CCFRS). Louvain method of community detection has been applied to discover communities in the dataset. The method of generating recommendations is based on the proposed idea of Item Frequency-Inverse Community Frequency (IF-ICF) score of each item in the target user's community. IF scores help finding the set of items which are unique to a particular community. ICF values are inversely proportional to the number of communities in which an item has been rated. It is used to calculate the uniqueness of the item across the communities. The IF-ICF scores of the items are further employed to find the prediction scores of items unseen by the user in order to present a set of top 'n' recommendations to the user. A prototype of the system is developed using Java and experimental analysis has been carried out for the domain of books. [ABSTRACT FROM AUTHOR]

Query completion service, normally known in the form of query auto completion (QAC) and widely provided by common search engines, assists users to formulate their queries after only typing few keystrokes. Previous work on QAC basically ranks query candidates according to their query popularity which is collected from the search logs, ignoring the internal semantic similarity between terms inside a query. However, we argue semantically related terms are apt to be combined when generating a query. In addition, as users often engage in QAC at word boundary (i.e., after typing a full word), we suppose that the time-aware popularity of the first word in a query candidate could affect the ranking of QAC candidates. Hence, based on the Markov assumption, we propose a new QAC ranking method, which models the QAC engagement as a Markov Chain and takes the semantic similarity between query terms into account. We contrast our proposed model with the traditional query popularity-based QAC approaches and verify its effectiveness in terms of Mean Reciprocal Rank (MRR). The experimental results show that our model significantly outperforms the baselines, achieving an average MRR improvement around 4% over the baselines. [ABSTRACT FROM AUTHOR]

Recommender Systems (RSs) are largely used nowadays to generate interest items or products for web users of diverse nature. Therefore, this work focuses on using fuzzy logic to accommodate diversity and uncertainty in user choices and interest. This would help in generating better recommendations with different tastes that correspond to different interest choices of the user. In this paper, a fuzzy hybrid multi-agent recommender system is designed and developed. The novelty of our approach is the use of interval type-2 fuzzy sets to create user models capable of capturing the inherent ambiguity of human behavior related to diverse users' tastes. In the due course, we also extended an existing, well known hybrid recommendation method, by integrating the proposed fuzzy approach into the recommendation process. As a result, a new RS approach was developed, which was capable of improving the prediction accuracy of system and at the same time reducing errors by being able to extract more information from the available dataset. Experimental study and analysis was conducted using two case studies namely book purchase and shopping women apparels. As a result, the proposed recommendation approach was found to perform considerably well as compared to its counterparts, even under data sparsity conditions. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) are useful tools for filtering and sorting items and information for users. There is a wide diversity of approaches that help creating personalized recommendations. Context-aware recommender systems (CARS) are a kind of RS which provide adaptation capabilities to the user’s environment, e.g., by sensing data through wearable devices or other biomedical sensors. In healthcare and wellbeing, CARS can support health promotion and health education, considering that each individual requires tailored intervention programs. Our research aims at proposing a context-aware mobile recommender system for the promotion of healthy habits. The system is adapted to the user’s needs, his/her health information, interests, time, location and lifestyles. In this paper, the CARS computational architecture and the user and context models of health promotion are presented, which were used to implement and test a prototype recommender system. [ABSTRACT FROM AUTHOR]

Recommender systems have increasingly become popular in various web environments (such as e-commerce and social media) for automatically generating items that match to individual users' personal interests. Among different types of recommender systems that have been developed so far, critiquing-based recommender systems have been widely recognized as an effective approach to obtaining users' feedback on the system's generated recommendations. Such systems have been demonstrated particularly helpful for serving new users. That is, by means of eliciting and refining their preferences through real-item feedback, the system is able to gradually improve its recommendation accuracy and aid users to make better decision. However, how to precisely acquire users' critiquing feedback is still a challenging issue. Most of existing systems rely on users to specify the feedback on their own, which unavoidably let users consume extra efforts. In our work, we have been engaged in analyzing users' eye-movement behavior when they evaluate recommendations, with the objective of identifying the correlation between eye movements and their critiquing feedback. The results can hence be constructive for developing an eye-based feedback elicitation method, so as to reduce users' self-critiquing efforts. Based on a collection of real users' eyegaze data, we have tested this idea's feasibility. Moreover, we have compared different recommendation interfaces (the interface that displays a set of recommended products), and found the category layout performs better than the list structure in terms of stimulating users to view recommended products. As a result, multiple design guidelines are derived from our user experiment. [ABSTRACT FROM AUTHOR]

Online judges are online systems that test solutions in programming contests and practice sessions. They tend to become large live repositories of problems, with hundreds, or even thousands, of problems. This wide problem statement availability becomes a challenge for new users who want to choose the next problem to solve depending on their knowledge. This is due to the fact that online judges usually lack meta information about the problems and the users do not express their own preferences either. Nevertheless, online judges collect a rich information about which problems have been attempted, and solved, by which users. In this paper, we consider all this information as a social network, and use social network analysis techniques for creating similarity metrics between problems that can be then used for recommendation. [ABSTRACT FROM AUTHOR]

A significant challenge being faced in recommender systems research concerns the provision of robust explanations about why a particular option is suggested. These explanations may exploit diverse data types concerning the users and items under consideration. In line with the above, this paper introduces a novel framework for automatic explanations building in recommender systems. The proposed solution follows a hybrid approach that meaningfully integrates collaborative filtering and sentiment analysis features into classical multi-attribute based ranking. A comprehensive evaluation of the proposed solution advocates the exploitation of additional and diverse information in explanation building, since this better fulfils a series of recommendation related aims such as transparency, persuasiveness, effectiveness and satisfaction. [ABSTRACT FROM AUTHOR]

Recommender Systems have become increasingly important and are applied in an increasing number of domains. While common collaborative methods measure similarity between different users, common content based methods measure similarity between different content. We propose a privacy aware recommender system that exploits relations present between entities appearing in content from user's history and entities appearing in candidate content. In order to identify such relations, we use the knowledge graph of NELL, which encodes entities and their relations. We present a novel normalized version of Personalized PageRank, to rank candidate content. We test our approach on the movie recommendation domain and show that the proposed method outperforms other baseline methods, including the standard Personalized PageRank. We intend to deploy our recommender system as a news recommendation app for mobile devices. [ABSTRACT FROM AUTHOR]

Recently, music recommender systems have been proposed to help users obtain the interested music. Traditional recommender systems making attempts to discover users' musical preferences by ratings always suffer from problems of rating diversity, rating sparsity and lack of ratings. These problems result in unsatisfactory recommendation results. To deal with traditional problems, in this paper, we propose a novel music recommender system, namely Multi-modal Music Recommender system (MMR), which integrates social and collaborative information to predict users' preferences. In this work, the playcounts are transformed into collaborative information to cope with problem of lack of rating information, while item tags and artist tags are employed as social information to cope with problems of rating diversity and rating sparsity. Through optimizing the integrated social-and-collaborative information, the users' preferences can be inferred more accurately and efficiently. The experimental results reveal that, three problems can be alleviated significantly and our proposed method outperforms other state-of-the-art recommender systems in terms of RMSE (Root Mean Square Error) and NDCG (Normalized Discount Cumulative Gain). [ABSTRACT FROM AUTHOR]

Trustworthy computing has recently attracted significant interest from researchers in several fields including multiagent systems, social network analysis, and recommender systems. As an additional dimension of information to past rating history, trust has been shown to be helpful for improving the accuracy of recommendations. Studies on the relationship between trust and rating behaviors may provide insights into the formation of trust in the context of online community, and lead to possible indicators for the effective use of trust in recommendations. In this paper, we study people's trust and rating behavior with the Epinions dataset. Epinions.com is a popular product review website allowing users to rate various categories of products, and establish a list of trustworthy users. We perform correlation analysis of activeness and trustworthiness defined by the number of ratings and the number of trustors to derive findings that can help the design of new decision support mechanisms in trust-based recommender systems. We then propose a trustee-influence based trust model where a trustee's activeness or trustworthiness is used to determine trust relationships. This trust model is incorporated into a memory-based and matrix factorization recommender systems to support online purchasing decision-making. Experimental results demonstrate the effectiveness of the proposed trust model for recommendation. [ABSTRACT FROM AUTHOR]

Recommender systems apply artificial intelligence techniques for filtering unseen information and predict whether a user would like/dislike a given item. K-Means clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple realtime algorithm that maintains a good level of accuracy, scale well with data, and build the training model incrementally with the arrival of new data. We run One-Pass algorithm on four different datasets (MovieLens, Film Trust, Book Crossing, and Last-FM) and empirically show that the proposed algorithm outperforms K-Means in terms of recommendation and training time. Moreover, One-Pass algorithm is comparable to K-Means in term of accuracy and cluster quality. [ABSTRACT FROM AUTHOR]

Nowadays, we are living in an information overload age. A tremendous amount of information has been produced on the Internet, how to find the interesting information is the main goal of recommendation system research. However, the most of current traditional recommendation algorithms (such as Collaborative filtering) are suffering from flowing difficulties: (i) The traditional recommendation system assume that users are independent and identically distributed; this assumption fails to consider the social relation and connection between users, which is not consistent with the social relations in our real world. (ii) Although there are some recommendation system research began to focus on the trust relationship between users, trust information is also very sparse. This leads to most of datasets only contains very little information about the user's relationship. In this paper, we propose an innovative method that integrated users' trust propagation and singular value decomposition into recommendation Algorithm to improve the quality of the recommendation effectively and efficiently. We performed our experiments on two real data sets respectively, the public domain Epinions.com and Filemtrust.com. The experimental results show that our method has a better outperform. [ABSTRACT FROM AUTHOR]

Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student's characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybridization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a new recommendation method based on argumentation theory that is able to combine content-based, collaborative and knowledge-based recommendation techniques, or to act as a new recommendation technique. This method provides the students with those objects for which the system is able to generate more arguments to justify their suitability. It has been implemented and tested in the Federation of Learning Objects Repositories of Colombia, getting promising results. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy recommender system (IFRS), which has been recently presented based on the theories of intuitionistic fuzzy sets and recommender systems, is an efficient tool for medical diagnosis. IFRS used the intuitionistic fuzzy similarity degree (IFSD) regarded as the generalization of the hard user-based, item-based and the rating-based similarity degrees in recommender systems to calculate the analogousness between patients in the system. In this paper, we firstly extend IFRS by using a new term - the intuitionistic fuzzy vector (IFV) instead of the existing intuitionistic fuzzy matrix (IFM) in IFRS. Then, the intuitionistic value similarity measure (IvSM) and the intuitionistic vector similarity measure (IVSM) are defined on the basis of the intuitionistic fuzzy vector. Some mathematical properties of these new terms are examined, and several IVSM functions are proposed. The performances of these IVSM functions for medical diagnosis are experimentally validated and compared with the existing similarity degrees of IFRS. The suggestion and recommendation of this paper involve the most efficient IVSM function(s) that should be used for medical diagnosis. [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify the current research status on linguistic decision making through visualization method. The effective information visualization tool called CiteSpace was used to dig out how the research of linguistic decision making was conducted. A number of 2017 documents published between 1980 and 2015 were downloaded via Web of Science with the keyword "linguistic decision making" was used for topic search. The reference co-citation network was mapped to explore the reprehensive documents and research clusters in linguistic decision making area. The author co-citation network was generated to reveal the influential scholars in this area. The journal co-citation map was formulated to identify the dominant journals. The category network was mapped to excavate the most popular research category in linguistic decision making area. The results of this study have great significance to the researchers in linguistic fuzzy set, linguistic decision making and linguistic group decision making areas. [ABSTRACT FROM AUTHOR]

M-Tree, Slim-Tree, DF-Tree, and Omni-Tree are some of the popular dynamic structures which can grow incrementally by splitting overflowed nodes, and adding new levels to the tree very much like the B-tree variants. Unfortunately, they have been shown to perform very poorly compared to flat structures such as AESA, LAESA, Spaghettis, and Kvp that use a fixed set of global pivots. HKvp index structure is an extension of Kvp allowing the elimination of pivots as well as the database objects. The number of pivots can be easily increased to provide more selectivity and query performance. However, there is an optimum number of pivots for a given query radius, and using too many pivots increases the costs of queries and index initialization. In this paper, a new set of pivot elimination mechanisms is proposed to determine the right number of pivots for different query radii. The suggested pivot elimination schemes perform significant cost reduction in terms of number of distance computations, and they estimate the drop rate value for HKvp on query time. [ABSTRACT FROM AUTHOR]

Discovering an effective team of experts toward accomplishing the specific task in social networks has been considered in many real projects. The communication and collaboration among the members and the small cardinality of the team are in the opposite direction to success of the projects. In this paper, we show that the type of a similarity function is also impressive. Its importance is revealed on determining what similar or dissimilar experts should be selected or rejected in the process of the assignment. Considering the graph of underlying social network as conceptual social networking websites, we attribute the team formation problem as a vertices similarity environment based on their common neighbors regarding their co-authored papers. Also, the implicit similarities are used with respect to inattention of additional intermediates between any two nodes in the graph. In addition, taking inspiration from human-human interactions, using just the implicit vertex similarities propose a collaborative recommendation that is based on the team formation framework. They can also identify effectors in social networks established by the structural equivalence relation. Thus, they make the algorithm faster on searching for members of the team. Moreover, the proficiency similarity measures of authors are considered as their potential characteristics that measure their skillfulness level and real contribution corresponding to the required skill. The combination of similarity measures in the cost function causes the algorithm to search the more effective team specially in equal situations. The experimental results on DBLP co-authorship graph show the effectiveness of using the new similarity measures in the proposed method. [ABSTRACT FROM AUTHOR]

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy-preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity. [ABSTRACT FROM AUTHOR]

Making recommendations in the presence of sparsity is known to present one of the most challenging problems faced by collaborative filtering methods. In this work we tackle this problem by exploiting the innately hierarchical structure of the item space following an approach inspired by the theory of Decomposability. We view the itemspace as a Nearly Decomposable system and we define blocks of closely related elements and corresponding indirect proximity components. We study the theoretical properties of the decomposition and we derive sufficient conditions that guarantee full item space coverage even in coldstart recommendation scenarios. A comprehensive set of experiments on the MovieLens and the Yahoo!R2Music datasets, using several widely applied performance metrics, support our model's theoretically predicted properties and verify that NCDREC outperforms several state-of-the-art algorithms, in terms of recommendation accuracy, diversity and sparseness insensitivity. [ABSTRACT FROM AUTHOR]

The World Wide Web is becoming the most important source to search for information or products. But the size and the unstructured nature of the available information makes the location of the right information a challenging task. Recommender systems and web usage mining techniques are two of the main methods used to overcome information overload. In this paper, we present a framework for the next page prediction that exploits users’ access history combined with his semantic interests to generate personalized and accurate recommendations. We are suggesting two different approaches for decision fusion between usage and semantic data. The two proposed techniques offered a 47.3% and 54.3% improvement in prediction accuracy over conventional methods for next page prediction. The suggested framework also employs user clustering to focus the search which reduced the prediction time by an average of 68.7% and 63.4%. [ABSTRACT FROM AUTHOR]

This paper provides an overview of our research with Pietro Torasso, outlining the main topics we worked at collaborating with him. Piero, as we are used to call him, had an extremely important role as an advisor, helping us to work in a methodologically sound way, with his constant and helpful feedback, suggestions, and forward-looking ideas. Describing our collaboration with Piero in a few pages is a difficult task, but we hope that this paper conveys at least a partial view of it. [ABSTRACT FROM AUTHOR]

Fine-grained opinions are often buried in user reviews. The opinionated aspects may also be associated with different weights by reviewers to represent the aspects' relative importance. As the opinions and weights provide valuable information about users' preferences for products, they can facilitate the generation of personalised recommendations. However, few studies to date have investigated the three inter-connected tasks in a unified framework: aspect identification, aspect-based rating inference and weight estimation. In this paper, we propose a unified framework for performing the three tasks, which involves 1) identifying the product aspects mentioned in a review, 2) inferring the reviewer's ratings for these aspects from the opinions s/he expressed in a review, and 3) estimating the reviewer's weights for these aspects. The relationship among these three tasks is inherently dependent in that the output of one task adjusts the accuracy of another task. We particularly develop an unsupervised model to Collectively estimate Aspect Ratings and Weights (shorted as CARW), which performs all of the three tasks by enhancing each other mutually. We conduct experiments on three real-life datasets to evaluate the CARW model. Experimental results show that the proposed model can achieve better performance than the related methods regarding each task. [ABSTRACT FROM AUTHOR]

Latency of user-based and item-based recommenders is evaluated. The two algorithms can deliver high quality predictions in dynamically changing environments. However, their response time depends not only on the size, but also on the structure of underlying datasets. This constitutes a major drawback when compared to two other competitive approaches i.e. content-based and modelbased systems. Therefore, we believe that there exists a need for comprehensive evaluation of the latency of the two algorithms. During a typical worst case scenario analysis of collaborative filtering algorithms two assumption are made. The first assumption says that data are stored in dense collections. The second assumption states that large amount of computations can be performed in advance during the training phase. As a result it is advised to deploy user-based system when the number of users is relatively small. Item-based algorithms are believed to have better technical properties when the number of items is small. We consider a situation in which the two assumptions are not necessarily met. We show that even though the latency of the two methods depends heavily on the proportion of users to items, this factor does not differentiate the two methods. We evaluate the algorithms with several real-life datasets. We augment the analysis with both graph-theoretical and experimental techniques. [ABSTRACT FROM AUTHOR]

Recommender systems employ machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Moreover, machine learning classifiers can be used for recommendation by training them on items' content information. These systems suffer from scalability, data sparsity, over specialisation, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed unique generalised switching hybrid recommendation algorithms that combine machine learning classifiers with the collaborative filtering recommender systems. We also provide various variants of the proposed algorithm by using Singular Value Decomposition (SVD) based recommendations, utilising SVD over collaborative filtering, and utilising SVD combined with Expected Maximisation (EM) algorithm. Experimental results on two different datasets, show that the proposed algorithms are scalable and provide better performance - in terms of accuracy and coverage - than other algorithms while at the same time eliminate some recorded problems with the recommender systems. [ABSTRACT FROM AUTHOR]

The rising urbanisation as well as the fact that work and leisure life become progressively geographically distributed lead to increased CO2 emissions from citizens' activities related to traffic and mobility. One promising method to reduce CO2 emissions from these activities is to encourage green transportation habits through recommendation and personalization technologies. Such technologies can support users in finding trips that cause low emissions and in the long term change their behaviour and habits. In this work we focus on travel recommenders and aim to provide urban travellers with a system that will nudge them to plan routes while considering the environmentally friendliest travel modes. We present a novel, ecologically-aware approach for travel recommender systems and describe our system implementation that incorporates dimensions of choice architecture. Our aim is to nudge users towards following routes that are environmentally friendly with lower CO2 emissions than of those they usually take. Our implementation leverages the routing options and results of a commercial routing engine rendering it potentially applicable to any city. Furthermore it is integrated in a route planning assistant for android mobile phones and everyday use through a client - server architecture. An initial qualitative evaluation with a selected group of users shows that the recommender provides useful results including routes with reduced CO2 emissions that users would not have considered without the recommender. [ABSTRACT FROM AUTHOR]

We present Interval-Rec, a recommender system that gives predictions on items that are rated on multiple criteria. Although a five-star rating system or similar linguistic scales are used typically by on-line sites to enable their users to rate items such as content or products, ratings are considered usually as ordinal and treated as ratio during the calculation of predicted ratings. We demonstrate that these symbolic or lexical semantics convey information about the strength of user preferences in addition to the order of the rated items. The methodology we propose considers and treats such scales as interval and in the same time provide accurate recommendations to users. Evaluations using well-known and reliable data showed improved results over other significant multi-criteria recommender systems and state of the art single criterion method. [ABSTRACT FROM AUTHOR]

Collaborative filtering is probably the most familiar and most widely implemented recommendation algorithm. However, traditional collaborative filtering methods focus only on rating data to generate recommendation; they do not consider useful information like item genre and evaluation time, which affect the quality of the system's recommendation seriously. In similarity computation, traditional algorithms use all items; they do not introduce genre component in correlation between user and item. Furthermore, they do not consider the influence of time on user's interests; giving the same treatment to user's score at different time. To address this issue, a new item-based collaborative filtering algorithm is proposed to exploit genre information in each item and reflect dynamic changes over time of user's preferences. The proposed algorithm endows each score with a weight function which keeps user's recent, long and periodic interest, and attenuate user's old short interest. Experimental results from Movielens data set show that the new algorithm outperforms the traditional item-based collaborative filtering algorithms. [ABSTRACT FROM AUTHOR]

Methods from the field of optimization theory have played an important role in developing training algorithms for matrix factorization in recommender systems. Indeed, the realization that simple stochastic unconstrained gradient descent can be applied with success to the factorization of the user-item matrix is responsible, to a great extent, for the recent research interest in this area, and the introduction of a plethora of matrix factorization methods. In this paper, motivated by earlier approaches in training neural networks, we introduce a constrained optimization framework for incorporating additional knowledge into the matrix factorization formalism, which can overcome certain drawbacks of the unconstrained minimization approach. We examine two types of such additional knowledge, and consequently derive two algorithms, as a result of incorporating the different knowledge types in the context of the constrained optimization framework. Both algorithms are designed to improve convergence and accuracy in the broader class of matrix factorization methods in recommender systems. [ABSTRACT FROM AUTHOR]

Ask the doctor services are personalized forums allowing patients to ask questions directly to doctors. Usually, patients must choose the most appropriate category for their question among lots of categories to be redirected to the most relevant physician. However, manual selection is tedious and error prone activity. In this work we propose to assist the patients in this task by recommending a short list of most appropriate categories. [ABSTRACT FROM AUTHOR]

Intelligent data analysis methods usually require as input a matrix, in which each row is an object to be analysed and each column is an attribute. In most cases it is assumed that attributes are Boolean, categorical or numerical. With the advent of semantic domain information in the form of ontologies, it is now common to find also semantic attributes, which may take as value a list of concepts. This paper proposes a new ontology-based procedure to compute the similarity between lists of semantic values, which may be used to compare objects. This measure is employed in an enhanced version of the k-means clustering method. The usefulness of the obtained classes has been tested in the context of a Web-based personalised recommender of Tourist destinations. [ABSTRACT FROM AUTHOR]

Social tagging provides an effective way for users to organize, manage, share and search for various kinds of resources. These tagging systems have resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, since social tags are generated by users in an uncontrolled way, they can be noisy and unreliable and thus exploiting them for recommendation is a non-trivial task. In this article,a new recommender system is proposed based on the similarities between user and item profiles. The approach here is to generate user and item profiles by discovering frequent user-generated tag patterns. We present a method for finding the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. In this way, the tag-base profiles are upgraded to semantic profiles by replacing tags with the corresponding ontology concepts. In addition, we further improve the semantic profiles through enriching them with a semantic spreading mechanism. To evaluate the performance of this proposed approach, a real dataset from The Del.icio.us website is used for empirical experiment. Experimental results demonstrate that the proposed approach provides a better representation of user interests and achieves better recommendation results in terms of precision and ranking accuracy as compared to existing methods. We further investigate the recommendation performance of the proposed approach in face of the cold start problem and the result confirms that the proposed approach can indeed be a remedy for the problem of cold start users and hence improving the quality of recommendations. [ABSTRACT FROM AUTHOR]

Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients, in order to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the task, which largely requires a deep knowledge of the financial domain, a recend trend in the area is to exploit recommendation technologies to support financial advisors and to improve the effectiveness of the process. This paper proposes a framework to support financial advisors in the task of providing clients with personalized investment strategies. Our methodology is based on the exploitation of case-based reasoning. A prototype version of the platform has been adopted to generate personalized portfolios, and the performance of the framework shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data.Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall. [ABSTRACT FROM AUTHOR]

Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations. [ABSTRACT FROM AUTHOR]

With the rapid increasing rate of the high volume of social web contents due to the growing popularity of social media services, significant attention has been drawn towards recommender systems i.e. systems, that offer recommendations to users on items appropriate to their requirements. To offer suitable recommendations, the systems need comprehensive user and item models that would be able to provide thorough understanding of their characteristics and preferences. In this article, a new recommender system is proposed based on the similarities between user and item profiles. The approach here is to generate user and item profiles by discovering frequent user-generated tag patterns, and to enrich each individual profile by a two-phase profile enrichment procedure. The profiles are extended by association rules discovered through the association rule mining process. The user/item profiles are further enriched through collaboration with other similar user/item profiles. To evaluate the performance of this proposed approach, a real dataset from The Del.icio.us website is used for empirical experiment. Experimental result s demonstrate that the proposed approach provides a better representation of user interests and achieves better recommendation results in terms of precision and ranking accuracy as compared to existing methods. [ABSTRACT FROM AUTHOR]

Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the cold-start phase, i.e., when no or few ratings are available for the items listed in the system catalogue and it is therefore hard to predict the missing ratings and compose relevant recommendations. This problem has not been directly tackled in previous research. Hence, in order to address it, we have designed and implemented several novel algorithmic components and interface elements in a fully operational points of interest (POI) mobile recommender system (STS). In particular, in this article we illustrate the benefits brought by using the user personality and active learning techniques. We have developed two extended versions of the matrix factorisation algorithm to identify what items the users could and should rate and to compose personalised recommendations. While context-aware recommender systems have been mostly evaluated offline, a testing scenario that suffers from many limitations, in our analysis we evaluate the proposed system in live user studies where the graphical user interface and the full interaction design play a major role. We have measured the system effectiveness in terms of several metrics such as: the quality and quantity of acquired ratings-in-context, the recommendation accuracy (MAE), the system precision, the perceived recommendation quality, the user choice satisfaction, and the system usability. The obtained results confirm that the proposed techniques can effectively overcome the identified cold-start problem. [ABSTRACT FROM AUTHOR]