A Temporal Usage Pattern-based Tag Recommendation Approach

While social tagging can benefit Internet users managing their resources, it suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag. Use of freely new tags and/or reuse of frequent tags have degraded coherence of corresponding resources of each tag that further frustrates people in retrieving information due to cognitive dissonance. Tag recommender systems can recommend users the most relevant tags to the resource they intend to annotate, and drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. Prior research on tag recommendation has addressed the time-dependence issues of tags by applying a time decaying measure to determine the recurrence probability of a tag according to its recency instead of its usage pattern. In response, this study intends to propose the temporal usage pattern-based tag recommendation technique to consider the usage patterns and temporal characteristic of tags for making recommendations

Folksonomy link prediction based on a tripartite graph for tag recommendation

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.Acknowledgement This publication was made possible by a grant from the Qatar National Research Fund NPRP 09-052-5-003.Scopu