Recommending Items in Social Tagging Systems Using Tag and Time Information

In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this candidate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation coming from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on data-sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure

An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents

The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the 201C;noise.201D; Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records

Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations

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

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Personalized tag recommender systems recommend a set of tags for items based on users’ historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models

Content Reuse and Interest Sharing in Tagging Communities

Tagging communities represent a subclass of a broader class of user-generated content-sharing online communities. In such communities users introduce and tag content for later use. Although recent studies advocate and attempt to harness social knowledge in this context by exploiting collaboration among users, little research has been done to quantify the current level of user collaboration in these communities. This paper introduces two metrics to quantify the level of collaboration: content reuse and shared interest. Using these two metrics, this paper shows that the current level of collaboration in CiteULike and Connotea is consistently low, which significantly limits the potential of harnessing the social knowledge in communities. This study also discusses implications of these findings in the context of recommendation and reputation systems.Comment: 6 pages, 6 figures, AAAI Spring Symposium on Social Information Processin

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure