A matrix factorization recommendation algorithm with time and type weight
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Abstract:
In order to solve the problem of information expiration of the recommender systems,we introduced the improved time weight of forgetting function and information retention period into matrix factorization model (MF)and proposed a MF-based and improved-time weighted collaborative filtering algorithm (MFTWCF)whose prediction accuracy had been raised by about 26.58% compared with that of neighborhood-based and improved-time weighted collaborative filtering algorithm(NTWCF). In view of the facts that users could continuously get access to some characteristics of past information, which would have greater influence for recommendation,we proposed the type weight to strengthen the information influence and to correct the improved time weight in MFTWCF. The new improved algorithm is called MF-based improved-time and type weighted collaborative filtering algorithm (MFTTWCF). The results of movie data set experiments show that the prediction accuracy of MFTTWCF algorithm is 3.58% higher than that of MFTWCF algorithm and can achieve better recommendation effect.And it is applicable to recommender systems with rating prediction.
