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A user-centric evaluation framework for recommender systems
- In RecSys ’11
, 2011
"... This paper explores the evaluation issues of recommender systems particularly from users ’ perspective. We first show results of literature surveys on human psychological decision theory and trust building in online environments. Based on the results, we propose an evaluation framework aimed at asse ..."
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Cited by 44 (4 self)
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This paper explores the evaluation issues of recommender systems particularly from users ’ perspective. We first show results of literature surveys on human psychological decision theory and trust building in online environments. Based on the results, we propose an evaluation framework aimed at assessing a recommender’s practical ability in providing decision support benefits to end-users from various aspects. It includes both accuracy/effort measures and a user-trust model of subjective constructs, and a corresponding sample questionnaire design. Author Keywords Recommender systems, user evaluation, adaptive decision theory, trust building, decision accuracy and effort. ACM Classification H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.
Categorising Social Tags to Improve Folksonomy-based Recommendations
- Journal of Web Semantics
, 2011
"... In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time ..."
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Cited by 19 (2 self)
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In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks. Current folksonomy-based search and recommendation models exploit the social tag space as a whole to retrieve those items relevant to a tag-based query or user profile, and do not take into consideration the purposes of tags. We hypothesise that a significant percentage of tags are noisy for content retrieval, and believe that the distinction of the personal intentions underlying the tags may be beneficial to improve the accuracy of search and recommendation processes. We present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Our approach finds 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. The obtained concepts are then
Connecting Users and Items with Weighted Tags for Personalized Item Recommendations
- In Proc. of HT’10
"... This is the author’s version of a work that was submitted/accepted for pub-lication in the following source: ..."
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Cited by 17 (7 self)
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This is the author’s version of a work that was submitted/accepted for pub-lication in the following source:
The role of tags for recommendation: a survey
"... Abstract — Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other pe ..."
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Cited by 8 (2 self)
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Abstract — Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other peers for browsing available resources. However, due to the absence of rules for managing the tagging process, and to the lack of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications dop not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing. For this reason researchers are modeling innovative recommender systems capable to better support tagging, browsing, and searching for new resources. This paper is a survey which discusses the role of tags in recommender systems: starting from social tagging systems, we analyze various techniques for suggesting content and we introduce the approaches exploited for proposing tags for classifying resources, considering both personalized and notpersonalized recommendation.
Web Usage Mining
, 2011
"... With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can ..."
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Cited by 6 (1 self)
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With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the life-time value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources. Web usage mining refers to the automatic discovery and analysis of patterns in clickstreams, user transactions and other associated data
Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations
- In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010, ACM: Toronto, Canada. 601
"... Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism—as opposed to Web search—for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social informat ..."
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Cited by 6 (0 self)
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Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism—as opposed to Web search—for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches.
User Graph Regularized Pairwise Matrix Factorization for Item Recommendation
"... Abstract. Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate us ..."
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Cited by 5 (1 self)
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Abstract. Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task.
Collaborative Topic Regression with Social Regularization for Tag Recommendation
"... Recently, tag recommendation (TR) has become a very hot research topic in data mining and related areas. However, neither co-occurrence based methods which only use the item-tag matrix nor content based methods which only use the item content information can achieve satisfactory performance in real ..."
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Cited by 3 (1 self)
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Recently, tag recommendation (TR) has become a very hot research topic in data mining and related areas. However, neither co-occurrence based methods which only use the item-tag matrix nor content based methods which only use the item content information can achieve satisfactory performance in real TR applications. Hence, how to effectively combine the item-tag matrix, item content information, and other auxiliary information into the same recommendation framework is the key challenge for TR. In this paper, we first adapt the collaborative topic regression (CTR) model, which has been successfully applied for article recommendation, to combine both item-tag matrix and item content information for TR. Furthermore, by extending CTR we propose a novel hierarchical Bayesian model, called CTR with social regularization (CTR-SR), to seamlessly integrate the item-tag matrix, item content information, and social networks between items into the same principled model. Experiments on real data demonstrate the effectiveness of our proposed models. 1
Using Inferred Tag Ratings to Improve User-based Collaborative Filtering
"... User-based collaborative filtering is one of the most widelyused recommender methods. It recommends items to a user according to her similar users ’ opinions. The key point of user-based collaborative filtering is to compute users ’ similarities. In traditional user-based collaborative filtering, th ..."
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Cited by 3 (2 self)
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User-based collaborative filtering is one of the most widelyused recommender methods. It recommends items to a user according to her similar users ’ opinions. The key point of user-based collaborative filtering is to compute users ’ similarities. In traditional user-based collaborative filtering, the similarity between two users is determined by their ratings to co-rated items. In some cases, two users rate few common items, such that the similarity between them may be inaccurate and it results in misleading recommendations. With the rapid development of social tagging systems, social tagging data poses new opportunities for recommender systems. Many researchers have proposed different methods to exploit tagging data to improve the performance of recommender systems. In this paper, we propose a new approach to compute users ’ similarities using the inferred tag ratings. A user’s preference for a tag t can be inferred based on her ratings of items tagged with t. A user rates too few such items, then her inferred rating to t may be inaccurate. Hence the relationships among tags are used to infer her preference for t based on all her item ratings, such that the preference of user could be accurate. Experiments were done on the MovieLens data set to evaluate the performance of our approach. The results show that our approach outperform traditional user-based collaborative filtering.
All the News that’s Fit to Read: A Study of Social Annotations for News Reading.
"... Figure 1. Despite the ubiquity of social annotations online, little is known about their effects on readers, and relative effectiveness. From left: (1) Facebook Social Reader showing articles friends recently read; (2) Google News Spotlight, algorithmic recommendations combined with annotations from ..."
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Figure 1. Despite the ubiquity of social annotations online, little is known about their effects on readers, and relative effectiveness. From left: (1) Facebook Social Reader showing articles friends recently read; (2) Google News Spotlight, algorithmic recommendations combined with annotations from friends; (3) New York Times recommendations for a logged-in user, from friends(top), and algorithms(bottom); (4) Facebook widget showing annotations from strangers for non-logged-in user. As news reading becomes more social, how do different types of annotations affect people’s selection of news articles? This paper reports on results from two experiments looking at social annotations in two different news reading contexts. The first experiment simulates a logged-out experience with annotations from strangers, a computer agent, and a branded company. Results indicate that, perhaps unsurprisingly, annotations by strangers have no persuasive effects. However, surprisingly, unknown branded companies still had a persuasive effect. The second experiment simulates a logged-in experience with annotations from friends, finding that friend annotations are both persuasive and improve user satisfaction over their article selections. In post-experiment interviews, we found that this increased satisfaction is due partly because of the context that annotations add. That is, friend annotations both help people decide what to read, and provide social context that improves engagement. Interviews also suggest subtle expertise effects. We discuss implications for design of social annotation systems and suggestions for future research.
