An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering

Abstract

With the popularization of the Internet and the prevalence of online marketing, e-commerce systems provide enterprises with unlimited display space and provide customers with more product choices, while its structure is becoming increasingly complex. The emergence and application of the network marketing recommendation system have greatly improved this series of problems. It can effectively retain customers, prevent customer loss, and increase the cross-selling volume of the e-commerce system. However, the current network marketing recommendation system is still immature in practical applications, and the problem of data sparseness is serious. The problem of user interest drift is not well dealt with, resulting in poor recommendation quality and poor real-time recommendation. Therefore, this paper proposes an online marketing recommendation algorithm based on the integration of content and collaborative filtering. First, content-based methods are used to discover users’ existing interests. After that, the mixed similarity model of content and behaviour is used to find the similar user group of the target user, predict the user’s interest in the feature words, and discover the user’s potential interest. Then, the user’s existing interest and potential interest are merged to obtain a user interest model that is both personalized and diverse. Finally, the similarity between the marketing content and the fusion model is calculated to form a set of user ratings combined with characteristics and then clustered through K-means to finally achieve recommendation. Experiments have proved that this method has good recommendation performance.

Application for Recommending Tourist Attractions on The Island of Java with Content Based Filtering Using Cosine Similarity

“…Thus, if the user changes, the technique with Content-Based Filtering is still possible to adjust the recommendation or suggestion of the appropriate item in a short time. The advantages and disadvantages of the Content-Based Filtering are: Advantages [11]:…”

“…New item can be recommended to users even though they don't have ratings from other users because they are based on the content of the item. Disadvantages [11]:…”

Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering

“…Other research [24], [28] focus on the use of social content in recommendation systems. In these studies, social content such as user reviews, ratings, or friend recommendations are used as additional information in CF and CBF algorithms.…”

“…In addition, content-based filtering is less dependent on historical user data, so it can work better in "cold start" situations where user data is limited. However, content-based filtering also has some challenges [23], [24]. One of the main challenges is the tendency to provide recommendations that focus on items with similar characteristics, which can lead to a lack of variety and diversity in recommendations.…”

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

“…The paper [61] suggested an algorithm for online marketing recommendations that integrate content and collaborative filtering. This fusion recommendation technique solves the new client problems by relying upon content filtering, sparsity of data based on collaborative filtering, and cold start challenge problems.…”