LBS-oriented individual recommendation method based on Markov prediction algorithm

Abstract

The invention discloses an LBS-oriented individual recommendation method based on the Markov prediction algorithm. Data filling is carried out by using the Slope One algorithm; therefore the sparsity problem is solved; the Markov prediction method and collaborative filtering recommendation are combined; contexts are classified according to the scores of interest points generated by users; the context transfer conditions of the users are recorded; time observation sequences are formed according to the context transfer conditions of the users; further, the context information of the users at the next moments of the users are predicted by using the Markov prediction method; in actual life, the geographic positions of the contexts have great influence on the transfer of the interest points of the users; in the process of calculating the similarity of the user contexts by using the collaborative filtering recommendation related techniques, the influence of the geographic positions on the similarity is fully combined; therefore, the quality of recommending the contexts to the users is greatly improved; this shows that compared with the traditional recommendation algorithm, the improved recommendation algorithm of the invention has great advantages and helps to improve the recommendation quality.

Description

technical field [0001] The invention belongs to the technical field of Internet communication, and in particular relates to a personalized recommendation method based on a Markov prediction algorithm. Background technique [0002] Many disciplines are involved in the study of context, and each discipline will have its own definition of context when it studies the context-aware system. At present, there is no unified definition of situation. Schilit and Theimer defined context as location, user, and surrounding people and objects, and considered context as a combination of the identity, temperature, location, and time of people around the user. Bazire and Brézillon define and examine situations in various domains. Ryan defines context as the user's focus, orientation, emotional state, location, date, surrounding people and objects. The current definition of situation is generally accepted by Schilit et al. in 1994. It is believed that the situation includes all entities th...

AI Technical Summary

Problems solved by technology

In foreign countries, the application of context-based information is gradually developing, while domestic research on context-based information is not yet mature

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