Search Results (3)

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems. Full article

In order to solve the problem of separation between consumer purchase and product experience in online sales, live streaming e-commerce came into being. However, the interaction of streamers is easy to cause consumers’ impulse consumption, which leads to the soaring return rate. In this context, how to make reasonable return policies to avoid the loss is an important issue for brands. This paper studies return policy selection for brands. We mainly focus on MCN (multi-channel network) click farming and customer disappointment aversion in the situations that the return-freight insurances are paid by brands or consumers or brands and MCN jointly. Three leader-follower models with brands as leaders and platforms and MCN as followers are established. To solve the above bilevel models, we discuss the conditions under which the upper and lower models are both convex and, based on these theoretical results, we give the optimal strategies for all members. Then, through numerical experiments, we analyze the impacts of customer disappointment aversion level, MCN’s ability, commission rate, brand’s return-freight insurance purchasing ratio, and other factors on each member’s optimal decision. The results show that the return policy in the situation of return-freight insurance paid by brand is suitable for a market with the high level of customer disappointment aversion; the return policy in the situation of return-freight insurance paid by consumers is applicable to the case of low customer disappointment aversion and high commission rate; the return policy in the situation of return-freight insurance paid by brand and MCN jointly is suitable for the case of low MCN capability and can effectively restrain the click farming from MCN. Full article

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications. Full article