Content-Based Recommendation Model in Micro-blogs Community

In this article, we present some novelty ideas to build a content-based recommendation system in micro-blogs community. We introduce a model to present users\´ preferences as a directed graph, named as "preference links", combining social relationships and people influences factors. Based on this model, we design an algorithm to collect recommendation candidates by visiting users\´ "preference links" and then generate a matrix to measure relevancies between content candidates and users\´ interests. A ranking function is proposed to rank these candidates based on the "relevancy matrix". We take the top items of the ranking results as the recommendation result. By implementing a prototype with these ideas in a real China micro-blogs community (Sina Weibo), our experiments show it can make personal recommendation with good accuracy.