Abstract :
The situations of context absence and sparse feature in short texts present a challenge to effective personalized service, especially in the recommend short texts, widened semantic gap between low-level text features representation and high-level interpretation. Meanwhile, the propagation characteristics of short texts also have an effect on the results of recommend short texts. However, the traditional methods of recommend short texts rarely take the above two aspects into account when recommending short texts to users. To solve the above problems, this paper presents a microblogging recommend method based on user model. This method maps the feature of microblogging to the semantic concept by Semantic Extension Method, then calculates the similarity of user model and semantic microblogging, furthermore calculates the factor of microblogging´s forwarding and comments, and lastly comprehensively considers the similarity and factor to recommend microblogging to users. Experiments show that the method of recommend microblogging based on user model is better than traditional methods. Users are more satisfied with the recommend results by user model than by traditional methods, and have a very high appraisal of this recommend method.
Keywords :
Web sites; collaborative filtering; recommender systems; text analysis; user modelling; context absence situations; high-level interpretation; low-level text feature representation; microblogging recommend method; personalized service; semantic extension method; semantic gap; semantic microblogging; short text propagation characteristics; short text recommendation; sparse feature; user model similarity; Accuracy; Context; Educational institutions; Games; History; Semantics; Vectors; propagation characteristics; recommend method; semantic extension; user model;
