Multi-Dimensional Recommendation Scheme for Social Networks Considering a User Relationship Strength Perspective

Developing a computational method based on user relationship strength for multi-dimensional recommendation is a significant challenge. The traditional recommendation methods have relatively low accuracy because they lack considering information from the perspective of user relationship strength into the recommendation algorithm. User relationship strength reflects the degree of closeness between two users, which can make the recommendation system more efficient between users in pairs. This paper proposes a multi-dimensional comprehensive recommendation method based on user relationship strength. We take three main factors into consideration, including the strength of user relationship, the similarity of entities, and the degree of user interest. First, we introduce a novel method to generate a user candidate set and an entity candidate set by calculating the relationship strength between two users and the similarity between two entities. Then, the algorithm will calculate the user interest degree of each user in the user candidate set to each entity in the entity candidate set, if the user interest degree is larger than or equal to a threshold, this particular entity will be recommended to this user. The performance of the proposed method was verified based on the real-world social network dataset and the e-commerce website dataset, and the experimental result suggests that this method can improve the recommendation accuracy

User Modeling on Twitter with WordNet Synsets and DBpedia Concepts for Personalized Recommendations

User modeling of individual users on the Social Web platforms such as Twitter plays a significant role in providing personalized recommendations and filtering interesting information from social streams. Recently, researchers proposed the use of concepts (e.g., DBpedia entities) for representing user interests instead of word-based approaches, since Knowledge Bases such as DBpedia provide cross-domain background knowledge about concepts, and thus can be used for extending user interest profiles. Even so, not all concepts can be covered by a Knowledge Base, especially in the case of microblogging platforms such as Twitter where new concepts/topics emerge everyday. In this short paper, instead of using concepts alone, we propose using synsets from WordNet and concepts from DBpedia for representing user interests. We evaluate our proposed user modeling strategies by comparing them with other bag-of-concepts approaches. The results show that using synsets and concepts together for representing user interests improves the quality of user modeling significantly in the context of link recommendations on Twitter