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In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user's or patient's illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user's extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. [ABSTRACT FROM AUTHOR]

Online shopping is a form of electronic commerce that enables customers to purchase products directly from the seller over the internet using a web browser or a mobile application. With the growing number of research papers on Deep Learning (DL)-based recommendations, DL models have proven to be effective in Recommender systems. Hence in this paper, relevant product accessories recommendation is proposed using deep learning and histogram features. The proposed recommendation describes the novel way of content-based filtering which divides the work into two phases. In phase 1, the implemented approach determines the color and category of the product. In phase 2, the recommendation system retrieves the matching accessories for the chosen product. For phase 1, Decision Tree, Linear Regression, Logistic Regression, Naïve Bayes, Long Short-Term Memory (LSTM), and a novel method of Stack dense Recurrent Neural Network (RNN) along with the color histogram is applied to identify the category and color of the product. It is justified that the proposed technique of stack dense RNN achieves 98% accuracy. In phase 2, from the Machine Learning (ML) perspective, Random Forest (RF) is employed for the recommendation. The RF model finds the matching accessories for the chosen product and retrieves the top relevant items. [ABSTRACT FROM AUTHOR]

Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. [ABSTRACT FROM AUTHOR]

Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users' specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. In addition to the two points mentioned above, most GNN-based models for recommendation have the following two limitations: First, the model's accuracy depends on the number of observed interactions in the dataset. Secondly, node representations are vulnerable to noisy interactions. This work has developed a novel recommendation model called "Multi-Attribute and Implicit Relationship Factors With Self-Supervised Learning for Collaborative filtering" (MIS-CF), which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. Meanwhile, an auxiliary self-supervised learning task is designed to help the downstream task optimize the node representation. MIS-CF aggregates multi-attribute spaces through the user-item bipartite graph and establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. The self-supervised learning task generates contrastive learning via self-discrimination, thus mining the rich auxiliary signals within the data, improving the accuracy and robustness of our model. Moreover, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance and robustness gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling. [ABSTRACT FROM AUTHOR]

Nowadays, the model compression method of knowledge distillation has drawn great attentions in Recommender systems (RS). The strategy of bidirectional distillation performs the bidirectional learning for both the teacher and the student models such that these two models can collaboratively improve with each other. However, this strategy cannot effectively exploit representation capabilities of each item and lack of the interpretability for the importance of items. Thus, how to develop an effective sampling scheme is still valuable for us to further study and explore. In this paper, we propose an improved rank discrepancy-aware item sampling strategy to enhance the performance of bidirectional distillation learning. Specifically, by employing the distillation loss, we train the teacher and student models to reflect the fact that a user has partiality for the unobserved items. Then, we propose the improved rank discrepancy-aware sampling strategy based on feedback learning mechanism to transfer just the useful information which can effectively enhance each other. The key part of the multiple distillation training aims to select valuable items which can be re-distilled in the network for training. The proposed technique can effectively solve the problem of high ambiguity in nature for recommender system. Experimental results on several real-world recommender system datasets well demonstrate that the improved bidirectional distillation strategy shows better performance. [ABSTRACT FROM AUTHOR]

Knowledge graphs have been introduced into recommender systems due to the rich connectivity information. Many knowledge-aware recommendation methods use graph neural networks (GNNs) to capture the high-order structural and semantic information of knowledge graphs. However, previous GNN-based methods have the following limitations: (1) they fail to make full use of the neighborhood information of entities and (2) they ignore the importance of user interaction sequences on reflecting user preferences. As such, these models are insufficient for generating accurate representations of users and items. In this study, we propose a Knowledge-aware Hierarchical Attention Network (KHAN) to provide better recommendation. Specifically, the proposed model mainly consists of an item encoder and a user encoder. The item encoder is equipped with a hierarchical attention network, which is used to generate entity (item) representations by carefully aggregating neighborhood information of entities. The user encoder is also designed to learn more informative user representations from user interaction sequences using multi-head self-attention. The learned user representations are then combined with user representations introduced in the item encoder through a gating mechanism to generate the final user representations. Extensive experiments on two real-world datasets about movie and restaurant recommendation demonstrate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure. [ABSTRACT FROM AUTHOR]

User rating information on multiple predefined aspects gathered by hotel recommendation systems generally shows a deviation between the overall rating and detailed criteria ratings. In this study, to address this deviation, we proposed a novel hotel recommendation method that clusters users with different preferences into different groups using the K-means algorithm. Moreover, we allocated weights to different criteria and obtained a comprehensive score. A case study on actual data from Tripadvisor.com showed that compared with three other models, our proposed model demonstrated a more impressive performance. This research can offer advantages to hotel service providers and customers in terms of decision making. [ABSTRACT FROM AUTHOR]

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones. [ABSTRACT FROM AUTHOR]

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage. [ABSTRACT FROM AUTHOR]

In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations. [ABSTRACT FROM AUTHOR]

Group recommender system provides suggestions for a group of users by exploring the choices of individual users of the group. Popularity of group recommender systems is increasing because many activities such as listening to music, watching movies, traveling, etc. are normally performed in groups rather individually. Group recommender systems like personal recommender systems also suffer from cold start and sparsity issues. The cold start and sparsity issues result into inaccurate recommendation computation which degrades the recommendation quality. To handle the cold start and sparsity issues in a Group Recommender System (GRS), this paper proposes to use cross domain approach and introduces Cross Domain Group Recommender System (CDGRS). The recommendations provided by trustworthy and reputed users in the group enhance the acceptance towards the presented recommendations as compared to the other individuals in the group. We have combined the social factors e.g. trust and reputation to get influential user in the group recommendation. A prototype of the system is developed for tourism domain that incorporates four sub-domains i.e. restaurants, hotels, tourist places and shopping places. The performance of CDGRS is compared with GRS. Spearman's Correlation Coefficient, MAE, RMSE, Precision, Recall and F-measure are used to find the accuracy of the generated recommendations. [ABSTRACT FROM AUTHOR]

Recommender Systems (RS) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid RSs combine ConF and ColF. We introduce an ontological hybrid RS where the ontology has been employed in its ConF part while improving the ontology structure by its ColF part. In this paper, a new hybrid approach is proposed based on the combination of demographic similarity and cosine similarity between users in order to solve the cold start problem of new user type. Also, a new approach is proposed based on the combination of ontological similarity and cosine similarity between items in order to solve the cold start problem of new item type. The main idea of the proposed method is to expand user/item profiles based on different strategies to build higher-performing profiles for users/items. The proposed method has been evaluated on a real dataset and the experimentations indicate the proposed method has the better performance comparing with the state of the art RS methods, especially in the case of the cold start. [ABSTRACT FROM AUTHOR]

With the popularity of networks and the increasing number of online users, recommender systems have suffered from the privacy leakage of sensitive information. While people enjoy recommender services, their information is exposed to the networks. To protect the privacy of users when using the recommender services, we propose a multi-level combined privacy-preserving model that maintains high accuracy of recommendation with privacy protection and alleviates the data sparsity problem. Our scheme contains two steps of recommendation. First, a multi-level combined random perturbation (MCRP) model is proposed on the client side. Our model dynamically divides multiple disturbance levels and adds noise of different ranges to the rating matrix according to Gaussian and uniform mixed disturbances. Second, on the server side, we propose a pseudo rating prediction filling (PRPF) algorithm based on the matrix factorization model. Combining the PRPF algorithm with the MCRP method significantly improves the recommender accuracy and effectively increases privacy security. Sensitive analysis and comparison experiments show that the proposed privacy method has certain advantages in security and recommender accuracy by using three publicly available datasets. [ABSTRACT FROM AUTHOR]

In the last decades, individual urban traffic flows have increased all over the world with a consequent growth of road congestion and environmental pollution. In this context, car-pooling is an interesting car-based alternative to satisfy the individual mobility demand by optimizing the car loading factor with respect to the number of passengers, provided that all the participants share trip origin and destination in the same time slot. To make the system more appealing, this paper proposes an on-demand car-pooling service adopting variable fares, on the basis of trip length and number of participants. Multi-agent, reputation and recommender system technologies in synergy with a routing algorithm have been used to this aim. Experiments on simulated data proved the potentiality of the proposed approach. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) has achieved great performance in recommender system over past decades. CF-based methods firstly map users and items to latent factors which share the same latent space, and then use a linear function to predict user ratings on items, such as inner product or cosine distance. It only uses original latent feature, however feature interactions are usually helpful in enhancing recommendation performance. To tackle such issue, we used Factorization Machines (FM) to enhanced linear methods by incorporating the second-order feature interactions. In this paper, we propose a novel hybrid model, AutoFM, which combine Denoising Autoencoder (DAE) and FM together. AutoFM follows collaborative filtering method, it firstly uses DAE to map users and items to latent factor, then it uses FM calculating user ratings on items. To tackle the cold start problem, we also take as the input of FM user's and item's side information besides of latent factor. We conduct AutoFM on three real-world datasets, and the experiment results show that AutoFM consistently outperforms the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Recommender Systems (RSs) are provided as web 2.0 services that give support to electronic commerce. Today, social information has been used for improving the performances of RSs. One of the popular social networks is Facebook which recently developed a new reaction button. This button provides new opportunists to analyze and understand the user's emotions and behavior. This paper proposes a new social-based RS that benefits from social information to improve the performance of the collaborative filtering. This RS depends on satisfaction degree and emotions of users. Each user can rate an item to express his satisfaction degree with this item. Also, the user able to express his feelings toward this item through the Facebook reaction button. The proposed algorithm is experimentally compared to alternative techniques. The results obtained shows that the proposed algorithm outperforms these algorithms in recommendation quality by 40% and performance by 29%. [ABSTRACT FROM AUTHOR]

Increase in chronic diseases among people gives the health care industry a challenging problem. Healthcare industry is using the Internet-of-Things (IoT) to create systems that monitor patients. The ultimate task of the system is to minimize manual efforts made to recommend food and drugs for chronic patients. However, many uncertain factors are involved with chronic patients, and existing models unable to handle it efficiently often lead to poor results. Medical records gathered from diverse devices, such as mobile and IoT devices that are raw in nature or in different formats cannot be utilized for further analysis. Since patient records grow rapidly, it is difficult for health care systems to manage and control. To overcome these limitations, the proposed system develops a fuzzy ontology-based recommender system using Type-2 fuzzy logic to recommend foods and drugs for chronic (diabetic) patient. Extraction of risk factors for chronic patients is achieved via wearable sensors and IoT-based electronic medical records are linked with linked open data (LOD) to create a knowledge base. Since, patient data sets are huge; cloud services are used to store and retrieve data for further analysis. An experiment is conducted on patient datasets and the results illustrate that the proposed work is efficient for patient data enrichment, risk factor extraction and appropriate medical advice for chronic patients. [ABSTRACT FROM AUTHOR]

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the problem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system's field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person's attentiveness, taste and distinguished behavior about them. We are considering m users, n items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach. [ABSTRACT FROM AUTHOR]

To address the problem that the existing time-aware methods always recommend out-of-fashion items to users, this paper proposes a novel method named Item Life Cycle based Collaborative Filtering (ItemLC-CF), taking both user's preference and item's popularity into consideration. This method presents a life cycle function to simulate the variation of item's popularity and integrates such variation with CF to learn the relative preference for items by reranking the candidate set of CF according to item's vitality. Given that it is extremely complicated to construct a life cycle function for each item, items are clustered into different categories via their popularities instead of original product categories. Meanwhile, a SVM ensemble classifier based on AdaBoost is employed to find an appropriate category for new arrival items. Evaluation on MovieLens-1M dataset demonstrates that the proposed method can improve the performance of recommendation list, in turn providing more reasonable and popular recommendation lists for users. [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]

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]

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]

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]

Recommender systems are changing from novelties used by a few E-commerce sites to serious business tools. They recommend products to customers based on their historical preferences. Through several recommendation techniques, Collaborative filtering (CF) is the most successful recommendation method which is widely used. Nowadays, customer lifetime value (CLV) is measured by RFM (Recency, Frequency, and Monetary) and weighted RFM-based method is used in product recommendation. In this paper, we present a product recommendation technique for online retail stores which employs CLV concept and integrates it with CF method to generate better quality recommendations. In this paper, CF is applied to customer ratings on products, which are collected implicitly by web usage mining approach. Product taxonomy is also used to segment products according to their categories and to reduce dimensions of computational space. The experimental results show that the proposed technique outperforms several other similar recommendation methods. [ABSTRACT FROM AUTHOR]

In our work, an agent-based system supporting workers in an organization is centered around utilization of ontologically demarcated data. In this system, ontological matchmaking, understood as a way of establishing closeness between resources, is one of key functionalities. Specifi- cally, it is used to autonomously provide recommendations to the user, who is represented by her/his personal agent. These recommendations specify, which among available resources are relevant/of interest to the worker. In this paper, we discuss our approach to measuring semantic closeness between ontologically demarcated information objects, while a Duty Trip Support application is used as a case study. General description of the algorithm is followed by a recommendation example based on support for a worker who is seeking advice in planning a duty trip. [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]

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]