Research On The Key Issues Of Personalized Recommendation And Search

Personalized information services are focusing on the fulfillment of the personalized information demands of different users based on their preference characteristics, behivor patterns, etc. Comparing with the traditional ones, personalized services could effectively cater to users' personal interests and correspondingly, they are widely accepted and becoming more and more popular. Lots of scholars and commercial organizations are paying their attentions to personalized services and many distinguished developments have been archieved in the past several years. In our paper, we present our research and discussion on two important techniques, the personalized recommendation and personalized search techniques. The main contributions are as follows:1. Focusing on the collaborative filtering process, we perform exploration and discussion for the new recommendation strategy. We present one novel method (Two-Level multiple Neural Networks-based Collaborative Filtering Recommendation Algorithm, TMNN-CFRA) for rating prediction in this paper. Multiple BP networks cooperating together, the higher layer neural networks propagates conversely the output deviation until to the lower layer neural networks to modify the network weights, and based on which, item recommendation prediction is accomplished by the forward process relying on the factors such as ratings, etc.. Experiment results on Movielens dataset show that TMNN-CFRA method is effective and feasible for item recommendation.2. Collaborative Filtering recommendation has cold-start problem. The root of the problem lies in that the ratings available are too limited, and recommendation system can not effectively mine users' preferences with so scarce data. In our paper, we present the basic but novel idea to alleviate the cold-start problem by taking advantage of the mining of implicit feedback data (two strategies referred). Relative to the traditional cold-start improvement methods focusing completely on the sparse data, our idea has its significance. It presents an effective perspective to alleviate cold-start problem—fully mining by using corresponding algorithms rather than omitting the valuable implicit feedback data like the traditional methods. We present two independent strategies to exploit the significance of making use of users'implicit feedback for cold-start problem. In the first strategy, we use BP neural network to learn the feedback data itself, by which to mine users'prefences towards the factors such as item slot, etc., from the "relative superiority or inferiority". In the second strategy, we make the basic but effective transformation for the available data, and by which, the similarity information will be skillfully abstracted from the implicit feedback and item ratings which are of no comparability originally. In most cases, the second strategy belonging to collaborative filtering category will be more effective for item recommendation than the first one which belongs to the content-based analysis category and the significance of users'implicit feedback for cold-start recommendation has been preliminary demonstrated in our experiments.3. The rapid expansion of web information greatly stimulates the demands for personalized domain search services. In our paper, we present the personalized vertical search algorithm (PVSA). Based on domain characteristics, PVSA relies on four strategies including domain topic preference vector, domain metadata weight factors and distinguishing different weights of input terms, etc., to mine and present different domain preferences of different users. Consequently, personalized search outputs are obtained. Experimental results show that our algorithm holds the promise of effectively providering the personalized search capacity for different users.4. Automated service composition and service recommendation are essential for semantic web research. Not the same as the completely ontology-dependent idea for service recommendation, in our paper, we present preference-based service recommendation algorithm (PSRA) mainly from statistics perspective. Firstly, PSRA filters out the ineffective succeeding services based on service semantics, and then performs the demographic similarity calculation based on the strategies such as occupation ontology, semantics distance, etc.. In the following, by integrating demographic factors with recommendation ratings, PSRA effectively persents the new and light-weighted similarity measurement. Lastly, based on the redefined similarites between users and for the same current service, PSRA presents different succeeding recommended services to different users to meet their personalized needs. Experimental results show that our algorithm is feasible and effective.