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17
A Study of Heterogeneity in Recommendations for a Social Music Service
, 2010
"... We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation. Aiming to identify which are the sources of information (ratings, tags, social contacts, etc.) most valuable for recommendation, we evaluate a number of content-based, collabor ..."
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We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation. Aiming to identify which are the sources of information (ratings, tags, social contacts, etc.) most valuable for recommendation, we evaluate a number of content-based, collaborative filtering and social recommenders on a heterogeneous dataset obtained from Last.fm. Moreover, aiming to investigate whether and how fusion of such information sources can benefit individual recommendation approaches, we propose various metrics to measure coverage, overlap, diversity and novelty between different sets of recommendations. The obtained results show that, in Last.fm, social tagging and explicit social networking information provide effective and heterogeneous item recommendations. Moreover, they give first insights on the feasibility of exploiting the above non performance recommendation characteristics by hybrid approaches.
Tagrec: Towards a standardized tag recommender benchmarking framework
- In Proc. of HT’14
, 2014
"... ABSTRACT In this paper, we introduce TagRec, a standardized tag recommender benchmarking framework implemented in Java. The purpose of TagRec is to provide researchers with a framework that supports all steps of the development process of a new tag recommendation algorithm in a reproducible way, in ..."
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ABSTRACT In this paper, we introduce TagRec, a standardized tag recommender benchmarking framework implemented in Java. The purpose of TagRec is to provide researchers with a framework that supports all steps of the development process of a new tag recommendation algorithm in a reproducible way, including methods for data preprocessing, data modeling, data analysis and recommender evaluation against state-of-the-art baseline approaches. We demonstrate the performance of the algorithms implemented in TagRec in terms of prediction quality and runtime using an extensive evaluation of a real-world folksonomy dataset. Furthermore, TagRec contains two novel tag recommendation approaches based on models derived from human cognition and human memory theories.
A comparative study of heterogeneous item recommendations in social systems
- INF. SCI
"... While recommendation approaches exploiting different input sources have started to proliferate in the literature, an explicit study of the effect of the combination of heterogeneous inputs is still missing. On the other hand, in this context there are sides to recommendation quality requiring furthe ..."
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While recommendation approaches exploiting different input sources have started to proliferate in the literature, an explicit study of the effect of the combination of heterogeneous inputs is still missing. On the other hand, in this context there are sides to recommendation quality requiring further characterisation and methodological research –a gap that is acknowledged in the field. We present a comparative study on the influence that different types of information available in social systems have on item recommendation. Aiming to identify which sources of user interest evidence –tags, social contacts, and user-item interaction data – are more effective to achieve useful recommendations, and in what aspect, we evaluate a number of content-based, collaborative filtering, and social recommenders on three datasets obtained from Delicious, Last.fm, and MovieLens. Aiming to determine whether and how combining such information sources may enhance over individual recommendation approaches, we extend the common accuracy-oriented evaluation practice with various metrics to measure further recommendation quality dimensions, namely coverage, diversity, novelty, overlap, and relative diversity between ranked item recommendations. We report empiric observations showing that exploiting tagging information by content-based recommenders provides high coverage and novelty, and combining social networking and collaborative filtering information by hybrid recommenders results in high accuracy and diversity. This, along with the fact that recommendation lists from the evaluated approaches had low overlap and relative diversity values between them, gives insights that meta-hybrid recommenders combining the above strategies may provide valuable, balanced item suggestions in terms of performance and non-performance metrics.
A Group Recommender for Movies Based on Content Similarity and Popularity
"... People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs o ..."
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People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs of a group (rather than the interests of individual group members). With a number of new movies being released every week, online recommenders play a significant role in suggesting movies for family members or groups of friends/people to watch, either at home or at movie theaters. Making group recommendations relevant to the joint interests of a group, however, is not a trivial task due to the diversity in preferences among group members. To address this issue, we introduce GroupReM which makes movie recommendations appealing (to a certain degree) to members of a group by (i) employing a merging strategy to explore individual group members ’ interests in movies and create a profile that reflects the preferences of the group on movies, (ii) using word-correlation factors to find movies similar in content, and (iii) considering the popularity of movies at a movie
Mining the Real-Time Web: A Novel Approach to Product Recommendation
- Knowl. Based Syst
, 2012
"... information Knowledge-Based Systems, 29 (May 2012): 3-11, Special Issue on Innovative techniques and applications of artificial intelligence ..."
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information Knowledge-Based Systems, 29 (May 2012): 3-11, Special Issue on Innovative techniques and applications of artificial intelligence
cTag: Semantic Contextualisation of Social Tags
- in Proceedings of the International Workshop on Semantic Adaptive Social Web, in connection with the 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011
, 2011
"... Abstract. In this paper, we present an algorithmic framework to identify the semantic meanings and contexts of social tags within a particular folksonomy, and exploit them for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with w ..."
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Abstract. In this paper, we present an algorithmic framework to identify the semantic meanings and contexts of social tags within a particular folksonomy, and exploit them for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies.
Learning Multiple Models for Exploiting Predictive Heterogeneity in Recommender Systems
"... Collaborative filtering approaches exploit information about historical affinities or ratings to predict unknown affinities between sets of “users ” and “items ” and make recommendations. However a model that also incorporates heterogeneous sources of information that may be available on the users a ..."
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Collaborative filtering approaches exploit information about historical affinities or ratings to predict unknown affinities between sets of “users ” and “items ” and make recommendations. However a model that also incorporates heterogeneous sources of information that may be available on the users and/or items can become a much more effective recommender, in terms of both increased relevance of the predictions as well as explainability of the results. In this paper, we propose a Bayesian approach that exploits not only such “side-information”, but also a different kind of heterogeneity that captures the variations in the mapping from user/item attributes to the affinities of interest. Such predictive heterogeneity is likely to occur in large recommender systems that involve a diverse set of users, and can be mitigated by using multiple localized predictive models rather than a single global one that covers all user-item pairs. The scope or coverage of each local model is determined simultaneously with the model parameters. The proposed approach can incorporate different types of inputs to predict the preferences of diverse users and items. We compare it against well-known alternative approaches and analyze the results in terms of both accuracy and interpretability. 1.
Content-based Semantic Tag Ranking for Recommendation
"... Abstract—Content-based social tagging recommendation, which considers the relationship between the tags and the descriptions contained in resources, is proposed to remedy the cold-start problem of collaborative filtering. There is such a common phenomenon that certain tag does not appear in the corr ..."
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Abstract—Content-based social tagging recommendation, which considers the relationship between the tags and the descriptions contained in resources, is proposed to remedy the cold-start problem of collaborative filtering. There is such a common phenomenon that certain tag does not appear in the corresponding description, however, they do semantically relate with each other. State-of-the-art methods seldom consider this phenomenon and thus still need to be improved. In this paper, we propose a novel content-based social tag ranking scheme, aiming to recommend the semantic tags that the descriptions may not contain. The scheme firstly acquires the quantized semantic relationships between words with empirical methods, then constructs the weighted tag-digraph based on the descriptions and acquired quantized semantics, and finally performs a modified graph-based ranking algorithm to refine the score of each candidate tag for recommendation. Experimental results on both English and Chinese datasets show that the proposed scheme performs better than several state-of-the-art content-based methods. Keywords-social tagging; recommender system; ranking I.
A Graph-based Recommendation across Heterogeneous Domains
, 2015
"... Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussio ..."
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Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommen-dation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely over-lapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.
Materials for K-12 and Advanced Readers
, 2014
"... This Dissertation is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact scholarsarchive@byu.edu. ..."
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This Dissertation is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact scholarsarchive@byu.edu.
