Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

Interaction-knowledge semantic alignment for recommendation

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user-item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users. In order to overcome the limitations mentioned above, we present a recommendation method named Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR). By introducing a semantic alignment module, the semantic difference between the interaction bipartite graph and the knowledge graph is reduced. The representation of user is integrated during the fusion of representations of item, which improves the quality of the fused representation of item. To validate the efficacy of the proposed approach, we perform comprehensive experiments on three datasets. The experimental results demonstrate that the IKSAR is superior to the existing methods, showcasing notable improvement.

G3SR: Global Graph Guided Session-Based Recommendation

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.

Graph Collaborative Filtering Based on Dual-Message Propagation Mechanism

The recommender system is a popular research topic in the past decades, and various models have been proposed. Among them, collaborative filtering (CF) is one of the most effective approaches. The underlying philosophy of CF is to capture and utilize two types of relationships among users/items, that is, the user-item preferences and the similarities among users/items, to make recommendations. In recent years, graph neural networks (GNNs) have gained popularity in many research fields, and in the recommendation field, GNN-based CF models have also been proposed, which are shown to have impressive performance. However, in our research, we observe a crucial drawback of these models, that is, while they can explicitly model and utilize the user-item preferences, the other necessary type of relationship, that is, the similarities among users/items, can only be implied and then utilized, which seems to hinder the performance of these models. Motivated by this, in this article, we first propose a novel dual-message propagation mechanism (DPM). The DPM can explicitly model and utilize both preferences and similarities to make recommendations; thus, it seems to be a better realization of CF's philosophy. Then, a dual-message graph CF (DGCF) model is proposed. Different from the existing models, in the DGCF, each user's/item's embedding is processed by two GNNs, with one handling the preferences and the other handling the similarities. Extensive experiments conducted on three real-world datasets demonstrate that DGCF substantially outperforms state-of-the-art CF models, and the small amount of sacrifice of time efficiency is tolerable considering the substantial improvement of model performance.

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

Design of Key Technologies for Elderly Public Network Services Based on Intelligent Recommendations

As the world's population continues to increase, the proportion of elderly people is also rising. The existing elderly public service system is no longer able to meet the needs of the elderly for their daily lives. The elderly population is significantly less receptive to emerging matters than the younger population, resulting in the public elderly service system not being able to access the initial data of elderly users in a timely manner, which causes the system to make incorrect recommendations. Therefore, the elderly cannot enjoy all kinds of online services provided by the Internet platform. In order to solve this problem, an elderly intelligent recommendation method based on hybrid collaborative filtering is proposed. First, the data of elderly users and elderly service items are scored, and modelling is completed by a collaborative filtering algorithm. Then, the XGBoost model is combined to solve the optimal objective function, so that the recommended data set with the highest score in the nearest neighbour set is obtained. The experimental results show that the proposed hybrid algorithm effectively solves the cold start phenomenon that occurs when the elderly population uses the web to make recommendations for elderly services. In addition, the proposed hybrid algorithm has a higher recommendation footprint accuracy than other recommendation algorithms.

An α-β-Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences

To quantify user-item preferences, a recommender system (RS) commonly adopts a high-dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative latent factor analysis model relying on a single latent factor (LF)-dependent, non-negative, and multiplicative update algorithm. However, existing models' representative abilities are limited due to their specialized learning objective. To address this issue, this study proposes an α- β -divergence-generalized model that enjoys fast convergence. Its ideas are three-fold: 1) generalizing its learning objective with α- β -divergence to achieve highly accurate representation of HiDS data; 2) incorporating a generalized momentum method into parameter learning for fast convergence; and 3) implementing self-adaptation of controllable hyperparameters for excellent practicability. Empirical studies on six HiDS matrices from real RSs demonstrate that compared with state-of-the-art LF models, the proposed one achieves significant accuracy and efficiency gain to estimate huge missing data in an HiDS matrix.

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.

An Autoencoder Framework With Attention Mechanism for Cross-Domain Recommendation

In recent years, the recommender system has been widely used in online platforms, which can extract useful information from giant volumes of data and recommend suitable items to the user according to user preferences. However, the recommender system usually suffers from sparsity and cold-start problems. Cross-domain recommendation, as a particular example of transfer learning, has been used to solve the aforementioned problems. However, many existing cross-domain recommendation approaches are based on matrix factorization, which can only learn the shallow and linear characteristics of users and items. Therefore, in this article, we propose a novel autoencoder framework with an attention mechanism (AAM) for cross-domain recommendation, which can transfer and fuse information between different domains and make a more accurate rating prediction. The main idea of the proposed framework lies in utilizing autoencoder, multilayer perceptron, and self-attention to extract user and item features, learn the user and item-latent factors, and fuse the user-latent factors from different domains, respectively. In addition, to learn the affinity of the user-latent factors between different domains in a multiaspect level, we also strengthen the self-attention mechanism by using multihead self-attention and propose AAM++. Experiments conducted on two real-world datasets empirically demonstrate that our proposed methods outperform the state-of-the-art methods in cross-domain recommendation and AAM++ performs better than AAM on sparse and large-scale datasets.

Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study, and analyzes the problems of "big data volume", "cold start" and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.

Cooperation Learning From Multiple Social Networks: Consistent and Complementary Perspectives

GWI survey¹ has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively. Indeed, users tend to express their views in more than one social media site. Hence, merging social signals of the same user across different social networks together, if available, can facilitate the downstream analyses. Previous work has paid little attention on modeling the cooperation among the following factors when fusing data from multiple social networks: 1) as data from different sources characterizes the characteristics of the same social user, the source consistency merits our attention; 2) due to their different functional emphases, some aspects of the same user captured by different social networks can be just complementary and results in the source complementarity; and 3) different sources can contribute differently to the user characterization and hence lead to the different source confidence. Toward this end, we propose a novel unified model, which co-regularizes source consistency, complementarity, and confidence to boost the learning performance with multiple social networks. In addition, we derived its theoretical solution and verified the model with the real-world application of user interest inference. Extensive experiments over several state-of-the-art competitors have justified the superiority of our model.¹http://tinyurl.com/zk6kgc9.