Development of Recommendation System Based on Ai Techniques to Exchange Items Between Users
Keywords:
Matrix Factorization-Based Technique, Online Bartering Platform Conventional TechniquesAbstract
Online bartering is becoming more and more commonplace as the Internet becomes more accessible. There are numerous parallels between recommending deals on an online bartering platform and conventional techniques to recommending products, including the requirement to model user preferences and product features. Bartering difficulties are intriguing and hard for various reasons, including the statement that users are providers and consumers, as well as the dynamic nature of the trading environment. Bartering needs us to understand more than simply the preferences of users, but also the dynamics of who trades with whom and at what time. We provide three new datasets from online bartering platforms to suggest new models for bartering-based recommendations. Existing solutions function poorly on real-world platforms because they depend on idealistic assumptions that are not supported by actual barter data. A Matrix Factorization-based technique is used to simulate the reciprocal interest that users have in each other’s things. Social and temporal relationships between members also have a significant impact, thus we expand our model to include these aspects. Our strategy is tested on a variety of markets, including book, video game, and beer transactions, and we see positive results compared to other strategies.
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