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Diversity plays a major role when a student is looking for a tutor to better understand some concepts or an entire course. In fact, algorithms for recommending potential tutors have to take into account several aspects of diversity that may be critical to successful tutoring. The tutor must have the appropriate competencies in the subject matter. In addition, he/she must be able to convey the knowledge and skills associated with the tutoring topic. Moreover, the personality traits of the tutee and the tutor can facilitate or hinder the learning process during tutoring. This study presents the experience of ‘SOS TUTORÍA UC’, a responsive web application aimed at facilitating academic assistance among students. Special emphasis is placed on the importance of incorporating dimensions of diversity that can inform the artificial intelligence algorithms of the potential tutor recommendation system. Indeed, competence in the topic tutored is the first diversity criterion for recommending more knowledgeable potential tutors. In addition, the tutee has to choose whether to look for tutors with personalities that are “different”, “similar” or “indifferent” to his or her own preferences for academic assistance on the specific topic. To achieve this, ‘SOS TUTORÍA UC’ is integrated with the WeNet platform, which provides user management services and user recommendation algorithms. The results of the testing of the recommendation system were positive with regard to the criterion of competence, while the criterion of personality should be addressed for improvement. In order to improve the tutor-tutee matching process, participants emphasized the importance of considering the criterion of personality traits in addition to competence. They also requested additional information and parameters to facilitate tutor selection. [ABSTRACT FROM AUTHOR]

The multi-layer feature pyramid structure, represented by FPN, is widely used in object detection. However, due to the aliasing effect brought by up-sampling, the current feature pyramid structure still has defects, such as loss of high-level feature information and weakening of low-level small object features. In this paper, we propose FI-FPN to solve these problems, which is mainly composed of a multi-receptive field fusion (MRF) module, contextual information filtering (CIF) module, and efficient semantic information fusion (ESF) module. Particularly, MRF stacks dilated convolutional layers and max-pooling layers to obtain receptive fields of different scales, reducing the information loss of high-level features; CIF introduces a channel attention mechanism, and the channel attention weights are reassigned; ESF introduces channel concatenation instead of element-wise operation for bottom-up feature fusion and alleviating aliasing effects, facilitating efficient information flow. Experiments show that under the ResNet50 backbone, our method improves the performance of Faster RCNN and RetinaNet by 3.5 and 4.6 mAP, respectively. Our method has competitive performance compared to other advanced methods. [ABSTRACT FROM AUTHOR]

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users' individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users' satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user's interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user's preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms. [ABSTRACT FROM AUTHOR]

Recent advancements in web personalization techniques facilitate enhanced web-based services that allow recommender systems (RSs) to incorporate contextual knowledge about users and items as an additional dimension into recommendation process. Context-awareness is one of the important aspects of ubiquitous computing to support cognitive environment and provide services in various e-commerce recommendation applications. Tracking each user's preferences over various contextual dimensions from their past transactions and providing personalized recommendations to them are the essence of context-aware recommender systems (CARSs). Conventional paradigms for incorporating context in recommendation process cannot fully cover the challenges on several levels of a context-aware system. Our proposed scheme is based on the hybridization of two complementary techniques, collaborative filtering (CF) and reclusive method (RM) to make context valuable at each level of users' preferences and improve predictive capability of CARSs. Further, a fuzzy real-coded genetic algorithm (Fuzzy-RCGA) approach is incorporated for identifying the influential contextual situations and handling the uncertainty of users' preferences under various contextual situations. Furthermore, users' demographic features are utilized for alleviating the problem of data sparsity. The empirical results on two real-world benchmark datasets clearly demonstrate the effectiveness of our proposed schemes for CARS framework. [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]

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]

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]

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]

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]

Recommendation systems are now widely used in many commercial applications. This tutorial focuses on the evaluation of such systems, from an application-oriented view. The tutorial recommends best practices, suggests a protocol for the evaluation process, and reviews a set of metrics that can be evaluated. The practices in this paper are motivated by similar procedures in nearby areas, such as machine learning, and information retrieval. [ABSTRACT FROM AUTHOR]

Constraint-based recommender systems support customers in preference construction processes related to complex products and services. In this context, utility constraints (scoring rules) play an important role. They determine the order in which items (products and services) are presented to customers. In many cases utility constraints are faulty, i.e., calculate rankings which are not expected and accepted by marketing and sales experts. The adaptation of these constraints is extremely time-consuming and often an error-prone process. We present an approach to the automated adaptation of utility constraint sets which is based on solutions for nonlinear optimization problems. This approach increases the applicability of constraint-based recommendation technologies by allowing the automated reproduction of example item rankings specified by marketing and sales experts. [ABSTRACT FROM AUTHOR]

“No matching product found” is an undesirable message for the user of an online product finder application or interactive recommender system. Query Relaxation is an approach to recovery from such retrieval failures and works by eliminating individual parts of the original query in order to find products that satisfy as many of the user's constraints as possible. In this paper, new techniques for the fast computation of “user-optimal” query relaxations are proposed. We show how the number of costly catalog queries can be minimized with the help of a query pre-processing approach, how we can compute relaxations that contain at least n items in the recommendation, and finally, how a recent conflict–detection algorithm can be applied for fast determination of preferred conflicts in interactive recovery scenarios. [ABSTRACT FROM AUTHOR]

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model, at the cost of a very small loss in overall performance, is able to recommend new items and give an explanation of its recommendations to its users. [ABSTRACT FROM AUTHOR]

Collaborative tagging systems allow users to assign keywords – so called “tags” – to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurrences. We show that both FolkRank and collaborative filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. [ABSTRACT FROM AUTHOR]

Collaborative filtering recommender systems are typically unable to generate adequate recommendations for newcomers. Empirical evidence suggests that the incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. Hence, users are highly encouraged to connect to other users to expand the trust network, but choosing whom to connect to is often a difficult task. Given the impact this choice has on the delivered recommendations, it is critical to guide newcomers through this early stage connection process. In this paper, we identify several classes of key figures in the trust network, namely mavens, frequent raters and connectors. Furthermore, we introduce measures to assess the influence of these users on the amount and the quality of the recommendations delivered by a trust-enhanced collaborative filtering recommender system. Experiments on a dataset from Epinions.com support the claim that generated recommendations for new users are more beneficial if they connect to an identified key figure compared to a random user. [ABSTRACT FROM AUTHOR]

Massive taxonomies for product classification are currently gaining popularity among e-commerce systems for diverse domains. For instance, Amazon.com maintains an entire plethora of hand-crafted taxonomies classifying books, movies, apparel and various other types of consumer goods. We use such taxonomic background knowledge for the computation of personalized recommendations, exploiting relationships between super-concepts and sub-concepts during profile generation. Empirical analysis, both offline and online, demonstrates our proposal's superiority over existing approaches when user information is sparse and implicit ratings prevail. Besides addressing the sparsity issue, we use parts of our taxonomy-based recommender framework for balancing and diversifying personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm. We evaluate our method using book recommendation data, including offline analysis on 361,349 ratings and an online study involving more than 2,100 subjects. [ABSTRACT FROM AUTHOR]

Conversational recommenders can help users find their most preferred item among a large range of options, a task that we call preference-based search. Motivated by studies in the field of behavioral decision theory, we take a user centric design perspective, focusing on the trade-off between decision accuracy and user effort. We consider example-critiquing, a methodology based on showing examples to the user and acquiring preferences in the form of critiques. In our approach critiques are volunteered in a mixed-initiative interaction. Some recommendations are suggestions specifically aimed at stimulating preference expression to acquire an accurate preference model. We propose a method to adapt the suggestions according to observations of the user's behavior. We evaluate the decision accuracy of our approach with both simulations exploiting logs of previous users of the system (in order to see how adaptive suggestions improve the process of preference elicitation) and surveys with real users where we compare our approach of example critiquing with an interface based on question-answering. [ABSTRACT FROM AUTHOR]

Various kinds of recommendation services open to the general public have recently been integrated into the website of the University Library of Karlsruhe as a test bed for information providers and e-commerce alike. This contribution reports on the development of RecoDiver, a graph-based user interface for behavior-based recommender systems. A Java applet integrated into the library's online catalog dynamically displays recommended further documents in a clickable graph centered around the document of interest to the user. A local view of the complete graph of recommendations is presented in a radial tree layout based on a minimum spanning tree with animated graph transitions featuring interpolations by polar coordinates to avoid crisscrossings. Further graph search tools like a selectable histogram of years of publication are available as well. This article portrays the user interface as well as the distributed web service architecture behind it and features an evaluation by user surveys showing the preference of users compared to the common lists of recommended items. [ABSTRACT FROM AUTHOR]

Recommendation techniques that analyze social trust networks attracted much attention in the last few years. They recommend such items that are appreciated by trusted friends. In this paper, we explore how to use trust information for generating personalized document recommendations such as for scientific papers or for webpages. The basic idea is to jointly analyze a trust network between readers who review the documents and the reference network between the documents. We develop trust-enhanced visibility measures for measuring the quality and the importance of documents and evaluate them in simulation studies. [ABSTRACT FROM AUTHOR]

With the advances in and popularity of mobile devices, mobile service providers have a direct channel for transferring information to their subscribers, i.e., short messaging service (SMS) and multimedia messaging service (MMS). Mobile service operators can recommend new content and information to users who opt in to receive such information directly through push messages at any time or place. However, as mobile push messages sent to users can cause interruptions, such as alarms, users who receive irrelevant push messages may become dissatisfied with their mobile Web service and even their service provider. In this paper, we propose a mobile content recommender system for sending personalized mobile push messages with content that users are likely to find relevant. This system learns users' preferences from contents and keywords in their usage logs and recommends items that match these preferences or those of similar users. We analyzed (a) customer feedback on personalized content dissemination, and (b) the relationship between customer feedback and mobile Web usage of customers subscribing to a Korean mobile service provider. Push messages with personalized recommendations resulted in more positive feedback from customers, and the mobile Web usage of these customers increased. [ABSTRACT FROM AUTHOR]

We propose a novel hybrid recommendation model in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies. The concept, item and user spaces are clustered in a coordinated way, and the resulting clusters are used to find similarities among individuals at multiple semantic layers. Such layers correspond to implicit Communities of Interest and enable enhanced recommendations. [ABSTRACT FROM AUTHOR]

The continuous growth in size and usage of the World Wide Web poses a number of challenging research problems. This thesis investigates the use of multidimensional data to automate the personalization and management activities of web sites. We pay particular attention to the use of additional dimensions (e.g., contextual or background information) in traditional user-item based top-N recommender systems and propose a multidimensional approach for this purpose. To support our research, we also propose a data warehouse to collect and compile information regarding the activity on a web site in terms of usage, content and structure. We show that, by exploiting multidimensional data, efficient web automation methods can be developed and used to improve the personalization and management of web sites. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on taxonomies for recommender systems, and another on trust-enhanced recommender systems.