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Design and implementation of restaurant recommendation system based on collaborative filtering hybrid algorithm

Design and Implementation of Restaurant Recommendation System Based on Hybrid Collaborative Filtering Algorithm

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Summary

The catering industry has always been an indispensable part of people's lives. With the popularization of the Internet and mobile devices, catering recommendation systems have become an important tool for users to find suitable catering places. However, traditional recommendation algorithms often have certain limitations when facing complex user needs and data situations.

This paper proposes a design and implementation of a restaurant recommendation system based on a collaborative filtering hybrid algorithm. First, through the collaborative filtering algorithm, the historical evaluation data of users on catering places is analyzed, and the user similarity matrix and the item similarity matrix are established to realize the association between users and items. Then, a hybrid algorithm is introduced to combine the content-based filtering algorithm and the neighborhood-based filtering algorithm to overcome the shortcomings of the traditional collaborative filtering algorithm such as the cold start problem and data sparsity, and improve the accuracy and recommendation quality of the recommendation system.

In the design and implementation phase, this paper uses the Java programming language to build a complete restaurant recommendation system prototype based on the Spring MVC framework and MySQL database. By understanding the user's behavioral characteristics, preferences, and historical evaluation data, and using collaborative filtering hybrid algorithms for recommendation, the system can provide users with personalized restaurant recommendations based on their needs and taste preferences. At the same time, the system also provides a user evaluation function, which can provide timely feedback on users' evaluation of restaurants and provide references for other users.

The experimental results show that the restaurant recommendation system based on the collaborative filtering hybrid algorithm has significant advantages over the traditional algorithm in terms of accuracy and recommendation quality. The system can better meet the personalized needs of users and improve their experience and satisfaction. In the future, this study can further optimize the algorithm performance and system functions, cover more application scenarios, and promote the development of restaurant recommendation systems.

Chapter 1 Introduction

1.1 Research Background

1.2 Research objectives

1.3 Research Methods

Chapter 2 Overview of Related Technologies

2.1 Collaborative Filtering Algorithm

2.2 Hybrid Algorithm

2.3 Restaurant Recommendation System

Chapter 3 System Design

3.1 System Architecture

3.2 Data Collection and Processing

3.3 User Feature Extraction

3.4 Recommendation Algorithm Design

Chapter 4 Algorithm Implementation and Optimization

4.1 Data Preprocessing

4.2 Collaborative Filtering Algorithm Implementation

4.3 Hybrid Algorithm Implementation

4.4 System Performance Optimization

Chapter 5 Experiment and Analysis

5.1 Experimental Setup

5.2 Experimental Results

5.3 Results Analysis

Chapter 6 Summary and Outlook

6.1 Summary of main work

6.2 Research Deficiencies and Prospects