Research on Personalized Movie Recommendation Based on User Portrait and Link Prediction
The overwhelming selection of movies on the market nowadays makes it difficult for users to make a decision.An efficient movie recommendation system plays a significant role in improving user experience and the market competitive-ness of movie service providers.The challenge lies in how to integrate multiple data sources for personalized recommendations while balancing algorithm accuracy and di-versity.Research on this issue is of great theoretical and practical significance.User portraits can depict rich user characteristics from multiple dimensions,helping us to better understand user interests and behaviors.Meanwhile,link prediction offers special benefits when modeling from a network topology standpoint.The integra-tion of them provides a possibility to solve the above issues.Therefore,this study proposes a novel user portrait and link prediction-based personalized recommenda-tion algorithm called UPLPR.The algorithm is designed under the background of movie recommendation.It distinguishes between the interest similarity that reflects in user behavior and genre domain.By abstracting user portraits from multiple data sources and integrating them into the link prediction process as external information of the network,the accuracy of the algorithm can be improved.Furthermore,from the perspective of scarcity,the algorithm improves the calculation of interest sim-ilarity between users in bipartite graph projection and evaluates the promoting or inhibiting effect of links in the recommendation process.Such consideration improves the novelty and personalization of the recommendation and mitigates the popularity bias problem to some extent.Finally,experiments were conducted on two Movie-Lens datasets to verify the proposed recommendation algorithm.Results show that compared with representative algorithms,the algorithm proposed in this paper not only achieved significant performance in accuracy but also demonstrated obvious ad-vantages in diversity-related indicators.Additionally,the abstracted user portraits can help recommendation platforms understand their user base,thereby formulating more scientific marketing and management strategies.
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