| 摘要: |
| 推荐系统在成熟的数据挖掘技术推动下, 已能高效地利用评分数据、行为轨迹等显隐性信息, 再与复杂而先进的深度学习技术相结合, 取得了很好的效果. 同时, 其应用需求也驱动着对基础数据的深度挖掘与利用, 以及对技术要求的减负成为一个研究热点. 基于此, 提出一种利用GCN (graph convolutional network)方法进行深度信息融合的轻量级推荐模型LG_APIF. 该模型结合行为记忆, 通过艾宾浩斯遗忘曲线模拟用户兴趣变化过程, 采用线性回归等相对轻量的传统方法挖掘项目的自适应周期等深度信息; 分析用户当前的兴趣分布, 计算项目的兴趣量, 以获取用户的潜在兴趣类型; 构建用户-类型-项目三元组的图结构, 并结合减负后的GCN技术来生成最终的项目推荐列表. 实验验证所提方法的有效性, 通过与8个经典模型在Last.fm, Douban, Yelp, MovieLens数据集中的对比, 表明该方法在Precision, Recall及NDCG指标上都得到良好改善, 其中, Precision平均提升2.11%, Recall平均提升1.01%, NDCG平均提升1.48%. |
| 关键词: 行为记忆 自适应周期 兴趣量因子 图卷积网络 推荐系统 |
| DOI：10.13328/j.cnki.jos.006915 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金(62262025); 江西省自然科学基金重点项目(20224ACB202012) |
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| Lightweight GCN Recommendation Combining Adaptive Period and Interest Factor |
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QIAN Zhong-Sheng, YE Zu-Lai, YAO Chang-Sen, ZHANG Ding, HUANG Heng, QIN Lang-Yue
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School of Information Management, Jiangxi University of Finance & Economics, Nanchang 330013, China
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| Abstract: |
| Driven by mature data mining technologies, the recommendation system has been able to efficiently utilize explicit and implicit information such as score data and behavior traces and then combine the information with complex and advanced deep learning technologies to achieve sound results. Meanwhile, its application requirements also drive the in-depth mining and utilization of basic data and the load reduction of technical requirements to become research hotspots. On this basis, a lightweight recommendation model, namely LG_APIF is proposed, which uses the graph convolutional network (GCN) method to deeply integrate information. According to behavior memory, the model employs Ebbinghaus forgetting curve to simulate the users’ interest change process and adopts linear regression and other relatively lightweight traditional methods to mine adaptive periods and other depth information of items. In addition, it analyzes users’ current interest distribution and calculates the interest value of the item to obtain users’ potential interest type. It further constructs the graph structure of the user-type-item triplet and uses GCN technology after load reduction to generate the final item recommendation list. The experiments have verified the effectiveness of the proposed method. Through the comparison with eight classical models on the datasets of Last.fm, Douban, Yelp, and MovieLens, it is found that the Precision, Recall, and NDCG of the proposed method are improved, with an average improvement of 2.11% on Precision, 1.01% on Recall, and 1.48% on NDCG, respectively. |
| Key words: behavior memory adaptive period interest factor graph convolutional network (GCN) recommendation system |
