A time-based approach to effective recommender systems using implicit feedback

Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available.

In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user’s purchase time and the item’s launch time in order to increase recommendation accuracy.

Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.     

Frequency-based similarity measure for multimedia recommender systems

Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item-based or user-based nearest neighborhood algorithms which utilize some kind of similarity measure to assess the similarity between different users or items for generating the recommendations. In this paper, we present a new similarity measure which is based on rating frequency and compare its performance with the current most commonly used similarity measures. The applicability and use of this similarity measure from the perspective of multimedia content recommendation is presented and discussed.     

A polynomial modeling based algorithm in top-N recommendation

Recommendation is the process of identifying and recommending items that are more likely to be of interest to a user. Recommender systems have been applied in variety of fields including e-commerce web pages to increase the sales through the page by making relevant recommendations to users. In this paper, we pose the problem of recommendation as an interpolation problem, which is not a trivial task due to the high dimensional structure of the data. Therefore, we deal with the issue of high dimension by representing the data with lower dimensions using High Dimensional Model Representation (HDMR) based algorithm. We combine this algorithm with the collaborative filtering philosophy to make recommendations using an analytical structure as the data model based on the purchase history matrix of the customers. The proposed approach is able to make a recommendation score for each item that have not been purchased by a customer which potentiates the power of the classical recommendations. Rather than using benchmark data sets for experimental assessments, we apply the proposed approach to a novel industrial data set obtained from an e-commerce web page from apparels domain to present its potential as a recommendation system. We test the accuracy of our recommender system with several pioneering methods in the literature. The experimental results demonstrate that the proposed approach makes recommendations that are of interest to users and shows better accuracy compared to state-of-the-art methods.     

An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data

Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.     

Timeliness in recommender systems

Due to the high efficiency in finding the most relevant online products for users from the information ocean, recommender systems have now been applied to many commercial web sites. Meanwhile, many recommendation algorithms have been developed to improve the recommendation accuracy and diversity. However, whether the recommended items are timely or not in these algorithms has not yet been well understood. To investigate this problem, we consider a temporal data division which divides the links to probe set and training set strictly according to the time stamp on links. We find that the recommendation accuracy of many algorithms are much lower in temporal data division than in the random data division.With a timeliness metric, we find that the low accuracy is caused by the tendency of these algorithms to recommend out-of-date items, which cannot be detected with the random data division. To solve this problem, we improve the considered recommendation algorithms with a timeliness factor. The resulting algorithms can strongly suppress the probability of recommending obsolete items. Meanwhile, the recommendation accuracy is substantially enhanced.     

Incentivized provision of metadata, semantic reasoning and time-driven filtering: Making a puzzle of personalized e-commerce

e-Commerce recommender systems select potentially interesting products for users by looking at their purchase histories and preferences. In order to compare the available products against those included in the user’s profile, semantics-based recommendation strategies consider metadata annotations that describe their main attributes. Besides, to ensure successful suggestions of products, these strategies adapt the recommendations as the user’s preferences evolve over time. Traditional approaches face two limitations related to the aforementioned features. First, product providers are not typically willing to take on the tedious task of annotating accurately a huge diversity of commercial items, thus leading to a substantial impoverishment of the personalization quality. Second, the adaptation process of the recommendations misses the time elapsed since the user has bought an item, which is an essential parameter that affects differently to each purchased product. This results in some pointless recommendations, e.g. including regularly items that the users are only willing to buy sporadically. In order to fight both limitations, we propose a personalized e-commerce system with two main features. On the one hand, we incentivize the users to provide high-quality metadata for commercial products; on the other, we explore a strategy that offers time-aware recommendations by combining semantic reasoning about these annotations with item-specific time functions. The synergetic effects derived from this combination lead to suggestions adapted to the particular needs of the users at any time. This approach has been experimentally validated with a set of users who accessed our personalized e-commerce system through a range of fixed and handheld consumer devices.     

Diversified recommendation method combining topic model and random walk

As one of the most widely used algorithms in recommendation field, collaborative filtering (CF) predicts the unknown rating of items based on similar neighbors. Although many CF-based recommendation methods have been proposed, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms to find similar neighbors. Secondly, there are many redundant similar items in the recommendation list generated by traditional CF algorithms, which cannot meet the user wide interest. Therefore, we propose a diversified recommendation method combining topic model and random walk. A weighted random walk model is presented to find all direct and indirect similar neighbors on the sparse data, improving the accuracy of rating prediction. By taking both users’ behavior data and items’ lags into account, we give a diversity measurement method based on the topic distribution of items discovered by Linked-LDA model. Furthermore, a diversified ranking algorithm is developed to balance the accuracy and diversity of recommendation results. We compare our method with six other recommendation methods on a real-world dataset. Experimental results show that our method outperforms the other methods and achieves the best personalized recommendation effect.     

Personalised Product Recommendation Model Based on User Interest

The scale of e-commerce systems is increasing and more and more products are being offered online. However, users must find their own desired products among a large amount of unrelated information, which makes it increasingly difficult for them to make a purchase. In order to solve this problem of information overload, and effectively assist e-commerce users to shop easily and conveniently, an e-commerce personalized recommendation system technology has been proposed. This paper introduces the design and implementation of a personalized product recommendation model based on user interest. The “shopping basket analysis” functional model centered on the Apriori algorithm uses the sales data in the transaction database to mine various interesting links between the products purchased by the customers. Moreover, it helps merchants to formulate marketing strategies, reasonably arranges shelf-guided sales, and attracts more customers. This platform adopts a B/S structure and uses JSP+AJAX technology to achieve the dynamic loading of pages. In the background, the Struts2 framework is combined with the SQL Server database to establish the system function module, and then the function is gradually improved according to the requirement analysis, and the development of the platform is finally completed.     

Designing social commerce platforms based on consumers’ intentions

Social commerce has been gaining momentum over the last few years as a novel form of e-commerce, creating substantial changes for both businesses and consumers. However, little is known about how consumer behaviour is influenced by characteristics on social commerce platforms. The purpose of this research is to elucidate how user intentions to purchase and to spread word-of-mouth (WOM) are influenced by characteristics present on social commerce platforms. More specifically, we adopt a uses-and-gratifications perspective and examine the influence of socialising, personal recommendation agents, product selection, and information availability. Partial least squares structural equation modelling analysis is performed on a sample of 165 social commerce users. Outcomes of the analysis indicate that socialising and personal recommendation agents positively influence purchase and WOM intentions, while product selection is found to only enhance purchase intentions. Interestingly, our findings reveal that information availability has no significant effect on purchase and WOM intentions. Finally, we find that when purchase intentions are triggered, they will tend increase consumers’ intentions to WOM.     

融合社交信息的局部潜在空间推荐方法

随着社交网络的发展,越来越多的研究利用社交信息来改进传统推荐算法的性能,然而现有的推荐算法大多忽略了用户兴趣的多样化,未考虑用户在不同社交维度中关心的层面不同,导致推荐质量较差.为了解决这个问题,提出了一种同时考虑全局潜在因子和不同子集特定潜在因子的推荐方法LSFS,使得推荐过程既考虑了用户共享偏好又考虑了用户在不同子集中的特定偏好.考虑到参与到不同社交维度的用户对不同的项目感兴趣,首先根据用户的社交关系将用户划分到不同的子集中;其次通过截断奇异值分解技术建模用户对项目的评分,其中全局潜在因子捕获用户共享的层面,而不同用户子集的特定潜在因子捕获用户关心的特定层面;最后,结合全局与局部潜在因子预测用户对未评分项目的评分.实验结果表明该方法可行且有效.     

结合用户信任模型的协同过滤推荐方法研究

协同过滤推荐是当前最成功的推荐技术之一,在电子商务推荐服务中得到了广泛的应用,它根据和目标用户具有相似行为的用户对项目的评价来进行推荐。鉴于传统的协同过滤推荐算法过于强调相似性的作用,并且和用户的认知习惯矛盾,引入了社会学中较成熟的信任机制来改进传统算法。实验结果表明,改进方法是有效的,它和传统的协同过滤推荐算法相比有更好的推荐质量。     

Application of covering rough granular computing model in collaborative filtering recommendation algorithm optimization

Data sparseness will reduce the accuracy and diversity of collaborative filtering recommendation algorithms. In response to this problem, using granular computing model to realize the nearest neighbor clustering, and a covering rough granular computing model for collaborative filtering recommendation algorithm optimization is proposed. First of all, our method is built on the historical record of the user's rating of the item, the user’s predilection threshold is set under the item type layer to find the user's local rough granular set to avoid data sparsity. Then it combines the similarity between users. Configuring the covering coefficient for target user layer, it obtained the global covering rough granular set of the target user. So it solved the local optimal problem caused by data sparsity. Completed the coarse–fine-grained conversion in the covering rough granular space, obtain a rough granular computing model with multiple granular covering of target users, it improved the diversity of the recommendation system. All in all, predict the target users’ score and have the recommendation. Compared experiments with six classic algorithms on the public MovieLens data set, the results showed that the optimized algorithm not only has enhanced robustness under the premise of equivalent time complexity, but also has significantly higher recommendation diversity as well as accuracy.     

Amazon.com recommendations: item-to-item collaborative filtering

Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.     

基于用户行为的个性化推荐系统的设计与应用①

目前电子商务网站提供的推荐服务很难满足用户的个性化需求,协同过滤算法作为应用最成功的推荐算法,依然存在数据稀疏性、用户评分真实性等问题,制约着推荐系统的质量。设计和实现了一个基于用户行为的个性化商品推荐系统,主要采用前融合组合推荐策略,避免了单纯使用协同过滤算法的弱点。阐述了基于用户行为的个性化推荐系统的设计思想和实现过程,最终通过实验验证了本推荐系统具有良好的推荐效果。     

综合社区与关联序列挖掘的电子政务推荐算法

个性化推荐作为一种有效的信息获取手段已成功应用于电商、音乐和电影等领域。已有研究多数聚焦于推荐的精度,缺乏对推荐结果的多样性考虑,忽略了应用领域中被推荐项目的流程特性（如"互联网+政务"中办事项的推荐）。为此提出一种综合用户社区与关联序列挖掘（CAS-UC）的电子政务推荐算法,优先向用户推送利益关联最大的办事项。首先,对用户和办事项的静态基本属性以及动态行为属性分别进行特征建模;其次,基于用户的历史办事记录和属性相似度进行用户社区发现,预筛选出与目标用户最为相似的用户集,提高推荐结果的多样性,减少核心推荐过程的计算量;最后,办事项的关联序列挖掘充分考虑了电子政务的业务特性,加入时间维度的办事项序列挖掘,进一步提高了推荐结果的精度。以芜湖市易户网为平台载体,基于Spark计算平台对用户脱敏后的信息进行仿真,实验结果表明,CAS-UC适用于被推荐项目具有序列或流程特性领域的推荐,与传统推荐算法如协同过滤推荐、矩阵分解以及基于语义相似度的推荐算法相比,具有更高的推荐精度,用户的多社区归属因素增加了推荐结果的多样性。     

Recommendation algorithm based on item quality and user rating preferences

Recommender systems are one of the most important technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filtering CF family, have problems such as scalability and sparseness. These problems hinder further developments of recommender systems. We propose a new recommendation algorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accuracy in rating prediction compared with the traditional approaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.     

User Personality and User Satisfaction with Recommender Systems

In this study, we show that individual users’ preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual preferences for recommendation diversity, popularity and serendipity by measuring their personality traits. We conducted an online experiment with over 1,800 users for six months on a live recommendation system. In this experiment, we asked users to evaluate a list of movie recommendations with different levels of diversity, popularity, and serendipity. Then, we assessed users’ personality traits using the Ten-item Personality Inventory (TIPI). We found that ratings-based recommender systems may often fail to deliver preferred levels of diversity, popularity, and serendipity for their users (e.g. users with high-serendipity preferences). We also found that users with different personalities have different preferences for these three recommendation properties. Our work suggests that we can improve user satisfaction when we integrate users’ personality traits into the process of generating recommendations.     

基于内容过滤的农资电子商务推荐系统

随着农业信息化的发展,农业类网站已经成为农业用户、合作社和农资公司等获取信息的重要渠道．结合中国现代化农资经营电子商务平台,提出了基于内容过滤的推荐技术,采用四元组构建用户偏好模型,引入遗忘因子挖掘和更新偏好模型,并根据产品模型和用户偏好模型的相似度向用户推荐产品．实验结果表明,基于内容过滤的推荐算法可使农资电子商务平台的产品浏览率和购买率得到提高．     

An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model

This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user's favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added.     

An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback

Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. In an earlier work, we introduced a recommendation system, termed Yoda, which employs a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy for large-scale Web-based applications. To reduce the complexity of the hybrid approach, Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows the real-time aggregation based on confidence values of an active user to pre-defined sets of recommendations. In this paper, we extend Yoda to include more recommendation sets. The recommendation sets can be obtained from different sources, such as human experts, web navigation patterns, and clusters of user evaluations.More over, the extended Yoda can learn the confidence values automatically by utilizing implicit users' relevance feedback through web navigations using genetic algorithms (GA). Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). The experimental results also indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.