Abstract
A personalized product recommendation is an effective mechanism to overcome information overload occurred when customers conduct Internet shopping. The paper presents a personalized recommender system, which integrates semantic similarity computation and TOPSIS method. First, semantic similarity is computed by constructing semantic vector-space, in order to realize the semantic content-based filtering between the product contents and customer profiles. Besides, TOPSIS method is also utilized to construct the comparison mechanism of products by calculating the utility value of each candidate product. Finally, the experiment is conducted to evaluate its recommender quality and the results show the system can give sensible recommendations and is capable of helping customers save enormous time for Internet shopping.
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