Friend Recommendation based on Hashtags Analysis
This addresses the need for more accurate friend recommendations for social network users by moving beyond exact hashtag matching to semantic analysis, though it is incremental as it builds on existing content-based methods.
The paper tackles the problem of friend recommendation in social networks by leveraging the semantics of hashtags, proposing a framework that constructs user profiles from hashtags, computes semantic similarity, and clusters users, achieving results on a dataset of 81,306 Twitter profiles.
Social networks include millions of users constantly looking for new relationships for personal or professional purposes. Social network sites recommend friends based on relationship features and content information. A significant part of information shared every day is spread in Hashtags. None of the existing content-based recommender systems uses the semantic of hashtags while suggesting new friends. Currently, hashtags are considered as strings without looking at their meanings. Social network sites group together people sharing exactly the same hashtags and never semantically close ones. We think that hashtags encapsulate some people interests. In this paper, we propose a framework showing how a recommender system can benefit from hashtags to enrich users' profiles. This framework consists of three main components: (1) constructing user's profile based on shared hashtags, (2) matching method that computes semantic similarity between profiles, (3) grouping semantically close users using clustering technics. The proposed framework has been tested on a Twitter dataset from the Stanford Large Network Dataset Collection consisting of 81306 profiles.