Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Edward Hinojosa-Cardenas,Lehi Quincho-Mamani,Edgar Sarmiento-Calisaya,Jair F,Cesar A

doi:10.14569/ijacsa.2020.0111083

Abstract

In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

Highlights

Huge amounts of user data are generated and collected every day on the web, given the explosive growth of information, users are often greeted with more than countless choices [1]
The new collaborative filtering technique is based on three components to recommend an item to a user: 1) clustering of the users; 2) a previous memory-based prediction; and 3) five decimal parameters
The MOEP-CF proposed method is based on a method proposed in [11], and hybrids Collaborative Filtering and Multi-Objective Evolutionary Programming

Summary

Introduction

Huge amounts of user data are generated and collected every day on the web, given the explosive growth of information, users are often greeted with more than countless choices [1]. Recommender system is an effective tool for helping the user in cutting the time needs to find personalised movies, products, documents, friends, places, services, among others [2]. A recommender system is one of the most important and new research area in machine learning [3]. The most commonly recommendation approaches [4] used to produce a list of items for a user are: content-based, collaborative filtering and hybrid approaches. Content-based filtering is based on the item to define the prediction, i.e., it uses features of the item to make a similar item recommendations. Collaborative filtering is one of the most prominent and popular approaches, It recommends similar items to similar users (similar users is based on past behavior, previous purchases, preferences, ratings of other products, average purchase amount, etc.).

Methods

Results

Conclusion