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.     

The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach

The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers’ satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers’ characteristics and the similarity of customers’ satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.     

Personalized recommendation based on review topics

The traditional collaborative filtering algorithm is a successful recommendation technology. The core idea of this algorithm is to calculate user or item similarity based on user ratings and then to predict ratings and recommend items based on similar users’ or similar items’ ratings. However, real applications face a problem of data sparsity because most users provide only a few ratings, such that the traditional collaborative filtering algorithm cannot produce satisfactory results. This paper proposes a new topic model-based similarity and two recommendation algorithms: user-based collaborative filtering with topic model algorithm (UCFTM, in this paper) and item-based collaborative filtering with topic model algorithm (ICFTM, in this paper). Each review is processed using the topic model to generate review topic allocations representing a user’s preference for a product’s different features. The UCFTM algorithm aggregates all topic allocations of reviews by the same user and calculates the user most valued features representing product features that the user most values. User similarity is calculated based on user most valued features, whereas ratings are predicted from similar users’ ratings. The ICFTM algorithm aggregates all topic allocations of reviews for the same product, and item most valued features representing the most valued features of the product are calculated. Item similarity is calculated based on item most valued features, whereas ratings are predicted from similar items’ ratings. Experiments on six data sets from Amazon indicate that when most users give only one review and one rating, our algorithms exhibit better prediction accuracy than other traditional collaborative filtering and state-of-the-art topic model-based recommendation algorithms.     

情感分析在电影推荐系统中的应用

基于用户的协同过滤推荐算法是通过分析用户行为寻找相似用户的集合,其核心是用户兴趣模型的建立以及用户间相似度的计算。传统的用户推荐算法是根据用户评分或者物品信息等行为数据进行个性化推荐,准确率比较低。充分考虑在线评论对于用户之间兴趣相似度的作用,通过对评论的情感分析,构建准确的用户兴趣模型,若用户在评论中表现出来的相似度越高,则表示用户之间的兴趣越相似。实验表明,和传统的基于用户的协同过滤推荐算法相比,基于评论情感分析的协同过滤推荐算法,无论准确率还是召回率都有明显提高。     

基于领域最近邻的协同过滤推荐算法

协同过滤是目前电子商务推荐系统中广泛应用的最成功的推荐技术,但面临严峻的用户评分数据稀疏性和推荐实时性挑战. 针对上述问题,提出了基于领域最近邻的协同过滤推荐算法,以用户评分项并集作为用户相似性计算基础,将并集中的非目标用户区分为无推荐能力和有推荐能力两种类型;对于前一类用户不再计算用户相似性以改善推荐实时性,对于后一类用户则提出“领域最近邻”方法对并集中的未评分项进行评分预测,从而降低数据稀疏性和提高最近邻寻找准确性. 实验结果表明,该算法能有效提高推荐质量.     

Discovery of automotive design paradigm using relevance feedback

According to the development of convergence technology and diversification of lifestyle, the importance of automotive design about sensibility products is rapidly increasing. According to the technical development, product performance and reliability of more than certain standards are recognized as basal conditions of market entry. And design and usability of products and subjective satisfaction are being magnified as the success factors of products. As functional performance including maximum speed, horsepower, and mileage reaches the satisfactory level in the automobile industry, consumers think the emotional aspects of the exterior and interior decor of automobiles as important. An analysis of customers’ sensibility and preferences is an important business strategy in the automobile industry that is increasingly becoming more customer oriented. In this paper, we proposed the discovery of automotive design paradigm using the relevance feedback. The proposed method applies a method that supports automotive design using the image-based collaborative filtering utilizing sensibility as a starting point in the development process. Although a collaborative filtering process can well predict the users’ interests and preferences, it cannot efficiently analyze design information while considering the contents of specific contents. The process extracts features from the image data that the users are interested to mitigate a problem, which is regarded as an undulated analysis, and then recommends an automotive design by combining the image filtering and collaborative filtering that use the static relationship between the users’ preference and the image color information. The proposed automotive design paradigm prediction system provides a recommendation by the image-based collaborative filtering. Paradigm recommendation according to sensibility and tendency is possible by applying the relevance feedback to react the paradigm which changes according to various lifestyles. Ultimately, this paper suggests empirical applications to verify the adequacy and validity of this system.     

Customized products recommendation based on probabilistic relevance model

Product customization is attracting more attentions in industry as a viable strategy to better meet customer requirements and gain more profit. However the vast number of product variants in product customization process often makes it difficult for consumers to make purchase decisions, a phenomenon referred to as information overload. In this paper we take a two-prong approach to tackle the issue of information overload in customized products recommendation. Basically, the method answers two questions, namely, which products to recommend and in what order to present the recommendations. Firstly, a probability relevance model is deployed to calculate the probability of relevance for each end product. Then a probability ranking principle is exploited to present the recommendations. The approach also takes customer flexibility into consideration and thus mitigates the effect of inconsistent specifications from customers. It does not require any prior knowledge about an active customer’s preference and can accommodate the new customers challenge facing by recommendation approaches. Analytical results show that the method is optimal in terms of customer’s utility and product recommendation efficiency. Numerical experiments are also conducted to test the presented approach.     

Rank order-based recommendation approach for multiple featured products

Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures.     

A Propound Hybrid Approach for Personalized Online Product Recommendations

ABSTRACT

The main aim of e-commerce websites is to turn their visitors into customers. For this purpose, recommender system is used as a tool that helps in turning clicks into purchases. Obtaining explicit ratings often faces problems such as authenticity of the ratings given by customers and queries that leads to low accuracy of the recommendations. Implicit ratings play a vital role in providing refined ranking of products. Preference level of the customers are predicted based on collaborative filtering (CF) approach using implicit details and mining click stream paths of like-minded users. Extracting the similarity among products using sequential patterns improves the accuracy of ranking. Integrating these two approaches improves the recommendation quality. Based on the results of experiment carried out to compare the performance of CF, sequential path of products viewed and integration of the two, we conclude that integration of mentioned approaches is superior to the existing ones.     

Typicality‐based collaborative filtering for book recommendation

Nowadays, personalized recommender system placed an important role to predict the customer needs, interest about particular product in various application domains, which is identified according to the product ratings. During this process, collaborative filtering (CF) has been utilized because it is one of familiar techniques in recommender systems. The conventional CF methods analyse historical interactions of user‐item pairs based on known ratings and then use these interactions to produce recommendations. The major challenge in CF is that it needs to calculate the similarity of each pair of users or items by observing the ratings of users on same item, whereas the typicality‐based CF determines the neighbours from user groups based on their typicality degree. Typicality‐based CF can predict the ratings of users with improved accuracy. However, to eliminate the cold start problem in the proposed recommender system, the demographic filtering method has been employed in addition to the typicality‐based CF. A weighted average scheme has been applied on the combined recommendation results of both typicality‐based CF and demographic‐based CF to produce the best recommendation result for the user. Thereby, the proposed system has been able to achieve a coverage ratio of more than 95%, which indicates that the system is able to provide better recommendation for the user from the available lot of products.     

基于客户喜好的双向个性化推荐算法

为了解决现有推荐算法仅考虑同类产品间单向推荐所缺乏的灵活性,提升产品的销量及用户的购物体验,提出一种基于客户喜好的双向个性化推荐算法,不仅可以为客户精准推荐产品,还可以为商家推荐潜在客户.首先,基于产品购买网络中客户及其邻居的购买信息,扩展客户购买信息;其次设计客户产品喜好权重计算办法,分析客户的购买喜好,并在客户喜好的指导下为客户提供个性化的产品推荐;最后,基于商家提供的样本客户,挖掘与样本客户相似的客户构成社区,为商家提供潜在客户推荐以及精准客户维护.在真实数据集上的实验验证了算法的有效性.该算法从客户和商家两个维度出发实现了产品与客户的双向推荐,为个性化推荐领域的研究提供有益的帮助.     

顾客偏好的动态挖掘算法

基于顾客偏好随时间变化的特性，采用聚类、关联规则等技术，对顾客偏好进行动态挖掘．通过追踪顾客购买序列，最终产生Top N产品推荐，旨在提高推荐系统的推荐质量．然后选取协同过滤算法作对照，并采用MovieLens站点提供的测试数据集．通过对召回率和精度两项指标的分析，表明该动态挖掘算法具有较高的推荐准确度和全面性．     

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.     

基于知识图谱的用户表征及在互补产品推荐中的应用

互补产品推荐旨在为用户提供经常一起购买的产品,以满足共同的需求。现有的互补产品推荐方法大多考虑对产品的内容特性(视觉和文本内容)建模,而没有考虑用户购买产品的偏好。为此设计了一种融合用户偏好的互补产品推荐模型(complementary product recommendation models that integrate user preferences, CPRUP)。该模型首先计算产品之间图像和文本特征的互补关系;然后将知识图谱与注意力机制相结合,基于n-hop邻居挖掘用户历史购买产品之间的相关性,提出一种基于知识图谱的用户表征来提取用户对互补产品的偏好;最后基于神经网络实现互补关系与用户偏好的共同学习。使用Amazon数据集进行实验,提出的CPRUP模型与次优基线模型相比,ACC提升了5%,precision提升了4%,表明CPRUP模型可以更准确地为用户推荐互补产品。     

A novel method for determining the key customer requirements and innovation goals in customer collaborative product innovation

Customer collaborative production innovation (CCPI) has become a worldwide new product design trend. The essential step to implement CCPI is to clear customer requirements and innovation goals for products. Based on the integration of traditional competitive priority ratings of customer requirements method for quality function deployment and grey relational analysis, this paper proposes a novel hybrid competitive priority ratings of customer requirements method for CCPI to identify the key customer requirements and innovation goals for a product. The method takes the heterogeneity of customers into consideration and allows different types of customers to assess customer requirements in their preferred or familiar formats which reflect their uncertainty degree. The proposed hybrid competitive priority ratings of customer requirements method represents a general approach for CCPI, does not require any transformation of multiform customers’ assessments that would cause information loss or information distortion. Its potential applications in determining the key customer requirements and innovation goals for CCPI are illustrated with a case study of smart phone development.     

基于商品特征的个性化推荐算法

针对现有个性化商品推荐算法精度不高、新商品不能及时推荐等缺点,提出了一种基于商品特征、用户购买日志及用户实时浏览行为的个性化推荐算法。算法首先根据客户的在线浏览情况获取当前客户的购买倾向,然后将客户的购买日志与商品特征数据库进行对比分析,获得客户对商品特征的偏爱度及推荐参照组,依据特征实体的相似度矩阵进行特征推荐组推荐,最后结合当前的购买倾向向客户推荐商品。     

适应用户兴趣变化的协同过滤推荐算法

协同过滤算法是至今为止最成功的个性化推荐技术之一,被应用到很多领域中.但传统协同过滤算法不能及时反映用户的兴趣变化.针对这个问题,提出两种改进度量：基于时间的数据权重和基于资源相似度的数据权重,在此基础上将它们有机结合,并将这两种权重引入基于资源的协同过滤算法的生成推荐过程中.实验表明,改进后的算法比传统协同过滤算法在推荐准确度上有明显提高.     

A novel collaborative filtering approach for recommending ranked items

In recent years, Collaborative Filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. Since current CF systems estimate the ratings of not-yet-rated items based on other items’ ratings, these CF systems fail to recommend products when users’ preferences are not expressed in numbers. In many practical situations, however, users’ preferences are represented by ranked lists rather than numbers, such as lists of movies ranked according to users’ preferences. Therefore, this study proposes a novel collaborative filtering methodology for product recommendation when the preference of each user is expressed by multiple ranked lists of items. Accordingly, a four-staged methodology is developed to predict the rankings of not-yet-ranked items for the active user. Finally, a series of experiments is performed, and the results indicate that the proposed methodology produces high-quality recommendations.     

Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data

When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings and they lack the capability to generate profits with value-added services. To tackle these two issues, we have built and evaluated a collaborative filtering recommender system which (a) makes individualized recommendations of potentially interesting value-added services when customers express interest in a particular physical product and also (b) leverages estimations of a customer’s willingness to pay to allow for a dynamic pricing of those services and the incorporation of profitability considerations into the recommendation process. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect individualized preference and willingness-to-pay data. Compared to other state-of-the-art approaches, our system requires significantly less customer input before making a recommendation, does not suffer from the usual sparseness of data and cold-start problems of collaborative filtering systems, and, as is shown in an empirical evaluation with a sample of 428 customers in the machine tool market, does not diminish the predictive accuracy of the recommendations offered.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.