scholarly journals Implicit Feedback Recommendation Method Based on User-Generated Content

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Fang ◽  
Enpeng Hu ◽  
Junyang Shen ◽  
Jingwen Zhang ◽  
Yang Chen
Keyword(s):  
Large Scale ◽  
Semantic Analysis ◽  
User Preferences ◽  
User Preference ◽  
User Generated Content ◽  
Implicit Feedback ◽  
Rating Data ◽  
Customer Needs ◽  
Sparsity Problem ◽  
Customer Need

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

scholarly journals A music recommendation algorithm based on clustering and latent factor model

2020 ◽  
Vol 309 ◽  
pp. 03009
Author(s):  
Yingjie Jin ◽  
Chunyan Han
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
User Preference ◽  
Implicit Feedback ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Latent Factor ◽  
Latent Factor Models ◽  
Feedback Data ◽  
Preference Matrix

The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in data sparseness. Meanwhile, implicit feedback data is more widely distributed than display score data, and relatively easy to collect, but implicit feedback data training efficiency is relatively low, usually lacking negative feedback. In order to effectively solve the above problems, we propose a music recommendation algorithm combining clustering and latent factor models. First, the user-music play record data is processed to generate a user-music matrix. The data is then analyzed using a latent factor probability model on the resulting matrix to obtain a user preference matrix U and a musical feature matrix V. On this basis, we use two K- means algorithms to perform user clustering and music clustering on two matrices. Finally, for the user preference matrix and the commodity feature matrix that complete the clustering, a user-based collaborative filtering algorithm is used for prediction. The experimental results show that the algorithm can reduce the running cost of large-scale data and improve the recommendation effect.

Implementing the Marketing Concept at the Employee-Customer Interface: The Role of Customer Need Knowledge

2009 ◽  
Vol 73 (4) ◽  
pp. 64-81 ◽  
Author(s):  
Christian Homburg ◽  
Jan Wieseke ◽  
Torsten Bornemann
Keyword(s):  
Customer Value ◽  
Large Scale ◽  
Customer Orientation ◽  
Cognitive Empathy ◽  
Frontline Employees ◽  
Marketing Concept ◽  
Frontline Employee ◽  
Customer Needs ◽  
Customer Interface ◽  
Customer Need

Although the identification of customer needs constitutes a cornerstone of the marketing concept, the accuracy of frontline employees’ perceptions of customer needs has never been examined in a systematic manner. Following research in social cognition, this article introduces the concept of “customer need knowledge” (CNK), which describes the extent to which a frontline employee can accurately identify a given customer's hierarchy of needs. The results of two large-scale, multilevel investigations involving data from three different levels (customers, employees, and managers) demonstrate the importance of CNK for the provision of customer satisfaction and customer value. In particular, CNK fully mediates the influence of employees’ customer orientation and cognitive empathy on these customer outcomes. Moreover, whereas the length of the relationship between an employee and a particular customer enhances CNK, a large age discrepancy in relation to the customer decreases employees’ level of CNK.

Learning from user interactions with rankings

2020 ◽  
Vol 54 (2) ◽  
pp. 1-2
Author(s):  
Harrie Oosterhuis
Keyword(s):  
Supervised Learning ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Learning To Rank ◽  
User Preferences ◽  
User Preference ◽  
Experimental Comparison ◽  
The Third ◽  
Ranking Systems

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learning to rank methods utilize expert-judgements to evaluate and learn, however, in many situations such judgements are impossible or infeasible to obtain. As a solution, methods have been introduced that perform learning to rank based on user clicks instead. The difficulty with clicks is that they are not only affected by user preferences, but also by what rankings were displayed. Therefore, these methods have to prevent being biased by other factors than user preference. This thesis concerns learning to rank methods based on user clicks and specifically aims to unify the different families of these methods. The first part of the thesis consists of three chapters that look at online learning to rank algorithms which learn by directly interacting with users. Its first chapter considers large scale evaluation and shows existing methods do not guarantee correctness and user experience, we then introduce a novel method that can guarantee both. The second chapter proposes a novel pairwise method for learning from clicks that contrasts with the previous prevalent dueling-bandit methods. Our experiments show that our pairwise method greatly outperforms the dueling-bandit approach. The third chapter further confirms these findings in an extensive experimental comparison, furthermore, we also show that the theory behind the dueling-bandit approach is unsound w.r.t. deterministic ranking systems. The second part of the thesis consists of four chapters that look at counterfactual learning to rank algorithms which learn from historically logged click data. Its first chapter takes the existing approach and makes it applicable to top- k settings where not all items can be displayed at once. It also shows that state-of-the-art supervised learning to rank methods can be applied in the counterfactual scenario. The second chapter introduces a method that combines the robust generalization of feature-based models with the high-performance specialization of tabular models. The third chapter looks at evaluation and introduces a method for finding the optimal logging policy that collects click data in a way that minimizes the variance of estimated ranking metrics. By applying this method during the gathering of clicks, one can turn counterfactual evaluation into online evaluation. The fourth chapter proposes a novel counterfactual estimator that considers the possibility that the logging policy has been updated during the gathering of click data. As a result, it can learn much more efficiently when deployed in an online scenario where interventions can take place. The resulting approach is thus both online and counterfactual, our experimental results show that its performance matches the state-of-the-art in both the online and the counterfactual scenario. As a whole, the second part of this thesis proposes a framework that bridges many gaps between areas of online, counterfactual, and supervised learning to rank. It has taken approaches, previously considered independent, and unified them into a single methodology for widely applicable and effective learning to rank from user clicks. Awarded by: University of Amsterdam, Amsterdam, The Netherlands. Supervised by: Maarten de Rijke. Available at: https://hdl.handle.net/11245.1/8ff3aa38-97fb-4d2a-8127-a29a03af4d5c.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

scholarly journals Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jun Li ◽  
Qiang Dong ◽  
Yan Fu
Keyword(s):  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Smart Devices ◽  
Data Sets ◽  
Rating Data ◽  
Temporal Effect ◽  
Proposed Model ◽  
Target User ◽  
The Difference

As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

scholarly journals SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

2020 ◽  
Vol 34 (04) ◽  
pp. 6127-6136
Author(s):  
Chao Wang ◽  
Hengshu Zhu ◽  
Chen Zhu ◽  
Chuan Qin ◽  
Hui Xiong
Keyword(s):  
Bayesian Approach ◽  
Posterior Probability ◽  
State Of The Art ◽  
User Preferences ◽  
Implicit Feedback ◽  
Pairwise Preference ◽  
Entire List ◽  
Collaborative Ranking ◽  
Real World Datasets ◽  
Made In

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

scholarly journals Evolutionary Social Poisson Factorizationfor Temporal Recommendation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
ChunYan Yin ◽  
YongHeng Chen ◽  
Wanli Zuo
Keyword(s):  
Recommendation Systems ◽  
Predictive Performance ◽  
User Preferences ◽  
Social Model ◽  
User Preference ◽  
Latent Factors ◽  
Factorization Model ◽  
Real World Datasets ◽  
Item Attributes ◽  
The Impact

AbstractPreference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF$$\_$$ _ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF$$\_$$ _ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.

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