基于协同过滤的文学推荐系统设计【源码+文档+部署】

基于协同过滤的文学推荐系统设计

摘要

随着信息技术的飞速发展和文学阅读需求的日益多样化，构建一个高效、精准的文学推荐系统变得尤为重要。本文采用Spring Boot框架，结合协同过滤算法，设计并实现了一个基于用户借阅行为和社交论坛互动的文学推荐系统。该系统综合考虑了用户信息、图书分类、图书详细信息以及用户在图书馆的借阅历史。通过深入挖掘用户在社交论坛中的互动数据，如论坛分类、帖子内容、回复情况等，系统能够更全面地理解用户的阅读偏好和潜在需求。在此基础上，系统运用协同过滤算法，根据用户的历史借阅记录和社交论坛行为，为用户推荐符合其兴趣和需求的文学作品。实验结果表明，该系统能够有效提升文学作品的推荐准确性，增强用户的阅读体验和满意度。通过结合社交论坛数据，系统还能够发现用户的潜在兴趣点，为图书馆提供有针对性的采购和服务建议。本文的研究不仅为文学推荐系统的设计和实现提供了有益的参考，也为图书馆服务模式的创新提供了新的思路。

关键词：文学推荐系统；Java 语言；MySQL 数据库；

Developing an Intelligent Literary Recommendation Framework Utilizing Collaborative Filtering Techniques

ABSTRACT

With the rapid development of information and technology and the increasing diversification of literary reading needs, it has become particularly important to build an efficient and accurate literary recommendation system. In this paper, a literary recommendation system based on user borrowing behavior and social forum interaction is designed and implemented by using the Spring Boot framework and the collaborative filtering algorithm. The system takes into account user information, book classification, book details, and the user's borrowing history in the library. By digging deep into the user's interaction data in social forums, such as forum classification, post content, and replies, the system can more comprehensively understand the user's reading preferences and potential needs. On this basis, the system uses a collaborative filtering algorithm to recommend literary works that meet the user's interests and needs based on the user's historical borrowing records and social forum behaviors. Experimental results show that the system can effectively improve the recommendation accuracy of literary works and enhance the reading experience and satisfaction of users. By combining data from social forums, the system is also able to discover users' potential interests and provide targeted purchasing and service recommendations for libraries. The research in this paper not only provides a useful reference for the design and implementation of the literary recommendation system, but also provides a new idea for the innovation of library service model.

Keywords: literary recommendation system; Java language; MySQL database;

目  录

第 1 章 绪论 5

1.1 研究背景 5 

1.2国内外发展现状 5 

1.3 研究意义 6 

1.4 论文设计框架 6 

第 2 章 系统开发技术 8

2.1 Spring Boot框架 8 

2.2 Java语言介绍 8 

2.3 VUE框架简介 8 2.4 协同过滤 8

第 3 章 系统分析 10

 3.1 可行性分析 10 

3.1.1 技术可行性 10

3.1.2 经济可行性 10

3.1.3 操作可行性 10 

3.1.4 法律可行性 10 

3.2 系统功能需求 11 

3.2.1 管理员功能需求 11

3.2.2 用户功能需求 12 

3.3 系统性能分析 12 

第 4 章 系统概要设计 13

4.1 系统结构设计 13 

4.2 系统顺序图设计 13 

4.3 系统流程设计 15

4.3.1 注册流程 15

4.3.2 登录流程 16  

4.4 数据库设计 16 

4.4.1 实体 E-R 图 16 

4.4.2 数据库表设计 19 

第 5 章 详细设计与实现 29

5.1 前台用户实现模块 29 

5.2 后台管理员实现模块 31  

第 6 章 系统测试 34

6.1 测试目的 34 

6.2 测试步骤 34 

6.3 测试原则 34 

6.4 测试结论 36 

结论与展望 37

参考文献 38

部分代码：

public R autoSort2(@RequestParam Map<String, Object> params,NewsEntity news, HttpServletRequest request){
        String userId = request.getSession().getAttribute("userId").toString();
        String inteltypeColumn = "typename";
        // 查询收藏集合
        List<StoreupEntity> storeups = storeupService.selectList(new EntityWrapper<StoreupEntity>().eq("type", 1).eq("userid", userId).eq("tablename", "news").orderBy("addtime", false));
        List<String> inteltypes = new ArrayList<String>();
        Integer limit = params.get("limit")==null?10:Integer.parseInt(params.get("limit").toString());
        List<NewsEntity> newsList = new ArrayList<NewsEntity>();
        //去重
        if(storeups!=null && storeups.size()>0) {
            List<String> typeList = new ArrayList<String>();
            for(StoreupEntity s : storeups) {
                if(typeList.contains(s.getInteltype())) continue;
                typeList.add(s.getInteltype());
                newsList.addAll(newsService.selectList(new EntityWrapper<NewsEntity>().eq(inteltypeColumn, s.getInteltype())));
            }
        }
        EntityWrapper<NewsEntity> ew = new EntityWrapper<NewsEntity>();
        params.put("sort", "id");
        params.put("order", "desc");
        // 根据协同结果查询结果并返回
        PageUtils page = newsService.queryPage(params, MPUtil.sort(MPUtil.between(MPUtil.likeOrEq(ew, news), params), params));
        List<NewsEntity> pageList = (List<NewsEntity>)page.getList();
        if(newsList.size()<limit) {
            int toAddNum = (limit-newsList.size())<=pageList.size()?(limit-newsList.size()):pageList.size();
            for(NewsEntity o1 : pageList) {
                boolean addFlag = true;
                for(NewsEntity o2 : newsList) {
                    if(o1.getId().intValue()==o2.getId().intValue()) {
                        addFlag = false;
                        break;
                    }
                }
                if(addFlag) {
                    newsList.add(o1);
                    if(--toAddNum==0) break;
                }
            }
        } else if(newsList.size()>limit) {
            newsList = newsList.subList(0, limit);
        }
        page.setList(newsList);
        return R.ok().put("data", page);
    }
}