Recommender System Based on Email Platform

• 1、Beijing University of Posts and Telecommunications, 100876

Abstract：Recommender systems are gaining a great importance with the emergence of E-commerce and business on the Internet. These recommender systems help users make decision by suggesting products and services that satisfy the users' tastes and preferences. As an important tool for Internet information exchange, e-mail plays a pivotal role in people's lives. Personalized recommendation in email platform becomes a new hot service. This paper presents a recommender system based on email platform. Our method makes a recommendation through extracting and analyzing user email information and user behavior logs. For every user, we will build a user model according to the user's email information, generating user interest matrix. The user interest matrix records the user's interests to things, including original interests and potential interests. After getting user interest matrix, we can recommend things to the user. Considering the drifting of user interest and the real-time of recommendation, we propose feedback recommendation. When a user clicks one thing, the system will get similar things and then recommend to the user. The similarity between things is stored in thing similarity matrix, which was calculated by content-based and collaborative filtering techniques. Besides, according to user email information and user behavior logs, we change the user interest matrix every once in a while tracking the change of user interests. What's more, as user interest matrix records user's potential interests, the recommendation results are diversity and novelty and the method gives a good recommendation for email platform.

keywords： Data Mining Recommender System Email Platform Interest Matrix

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