Cross Domain Framework for Implementing Recommendation Systems Based on Context Based Implicit Negative Feedback

2012 ◽  
Vol 3 (1) ◽  
pp. 22-36
Author(s):  
Maitri Jhaveri ◽  
Jyoti Pareek
Keyword(s):  
Negative Feedback ◽  
Information Overload ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
P2p Networks ◽  
Cross Domain ◽  
Collaborative Environment ◽  
Available Information ◽  
Novel Model

The last decade met a remarkable proliferation of P2P networks, PDMS, semantic web, communitarian websites, electronic stores, etc. resulting in an overload of available information. One of the solutions to this information overload problem is using efficient tools such as the recommender system which is a personalization system that helps users to find items of interest based on their preferences. Several such recommendation engines do exist under different domains. However these recommendation systems are not very effective due to several issues like lack of data, changing data, changing user preferences, and unpredictable items. This paper proposes a novel model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. This model is based on studying implicit negative feedback from users in cross domain collaborative environment to identify user preferences effectively. The authors have also identified a list of parameters for this study.

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.

scholarly journals Hybrid POI Recommendation System for Tourism

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Monishkanna Barathan ◽  
Ershad Sharifahmadian
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Interaction ◽  
User Preferences ◽  
Knowledge Based ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Information Finding ◽  
Hybrid Recommender ◽  
Available Information

Due to the increase in amount of available information, finding places and planning of the activities to be done during a tour can be strenuous. Tourists are looking for information about a place in which they have not been before, which worsen the selection of places that fit better with user’s preferences. Recommendation systems have been fundamentally applicable in tourism, suggest suitable places, and effectively prune large information from different locations, so tourists are directed toward those places where are matched with their needs and preferences. Several techniques have been studied for point-of-interest (POI) recommendation, including content-based which builds based on user preferences, collaborative filtering which exploits the behavior of other users, and different places, knowledge-based method, and several other techniques. These methods are vulnerable to some limitations and shortcomings related to recommendation environment such as scalability, sparsity, first-rater or gray sheep problems. This paper tries to identify the drawbacks that prevent wide spread use of these methodologies in recommendation. To improve performance of recommendation systems, these methods are combined to form hybrid recommenders. This paper proposes a novel hybrid recommender system which suggests tourism destinations to a user with minimal user interaction. Furthermore, we use sentiment analysis of user’s comments to enhance the efficiency of the proposed system.

Personalization Approaches for Ranking

2017 ◽  
Vol 7 (1) ◽  
pp. 1-16
Author(s):  
Madhuri A. Potey ◽  
Pradeep K. Sinha
Keyword(s):  
User Satisfaction ◽  
State Of The Art ◽  
User Model ◽  
Research Trends ◽  
User Preferences ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Information Need ◽  
And Behavior ◽  
Available Information

Search engine technologies are evolving to satisfy the user's ever increasing information need; but are yet to achieve perfection especially in ranking. With the exponential growth in the available information on the internet; ranking has become vital for satisfactory search experience. User satisfaction can be ensured to some extent by personalizing the search results based on user preferences which can be explicitly stated or learned from user's search behavior. Machine learning algorithms which predict user preference from the available information related to the user are extensively experimented for personalization. Among several studies undertaken for re-ranking the documents, many focus on the user. Such approaches create user model to capture the search context and behavior. This study attempts to analyze the research trends in user model based personalization and discuss experimental results in personalized information retrieval area. The authors experimented to extend the state of the art in the specific areas of personalization.

Semantic-Driven Information Recommendation System

2013 ◽  
Vol 475-476 ◽  
pp. 1226-1229
Author(s):  
Zhen Hua Huang ◽  
Qiang Fang
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Recommendation Systems ◽  
User Preference ◽  
Cloud Architecture ◽  
Computation Theory ◽  
Information Recommendation ◽  
Indexing Mechanism ◽  
Semantic Computation ◽  
The One

Information recommendation systems is the one of the most effective tools to solve the problem of information overload. In this paper, we design SIRSCA, a semantic-driven information recommendation system under cloud architecture. SIRSCA mainly includes four modules: semantics representation of foundation data and user preference informations; indexing mechanism of massive semantic informations under cloud architecture; recommendation approaches based on semantic computation theory; and technologies of dynamic migration under cloud architecture.

scholarly journals A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhao Huang ◽  
Pavel Stakhiyevich
Keyword(s):  
Hybrid Approach ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
User Profiles ◽  
Group Recommendation ◽  
Group Recommendations ◽  
Time Awareness ◽  
Hybrid Recommendation ◽  
Time Aware

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

scholarly journals A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hongzhi Li ◽  
Dezhi Han
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Clustering Algorithm ◽  
Information Overload ◽  
User Feedback ◽  
User Preferences ◽  
User Preference ◽  
Logistic Regression Algorithm ◽  
Hybrid Recommendation ◽  
Time Aware

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

Revisiting Recommendation Systems

2013 ◽  
pp. 129-151
Author(s):  
Sagarika Bakshi ◽  
Sweta Sarkar ◽  
Alok Kumar Jagadev ◽  
Satchidananda Dehuri
Keyword(s):  
Recommender Systems ◽  
Significant Role ◽  
Information Overload ◽  
Recommendation Systems ◽  
Hybrid Models ◽  
User Preference ◽  
Individual Approach ◽  
Insight Into

Recommender systems are applied in a multitude of spheres and have a significant role in reduction of information overload on those websites that have the features of voting. Therefore, it is an urgent need for them to adapt and respond to immediate changes in user preference. To overcome the shortcomings of each individual approach to design the recommender systems, a myriad of ways to coalesce different recommender systems are proposed by researchers. In this chapter, the authors have presented an insight into the design of recommender systems developed, namely content-based and collaborative recommendations, their evaluation, their lacunae, and some hybrid models to enhance the quality of prediction.

scholarly journals A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rongmei Zhao ◽  
Xi Xiong ◽  
Xia Zu ◽  
Shenggen Ju ◽  
Zhongzhi Li ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation System ◽  
Information Overload ◽  
User Behavior ◽  
Recommendation Systems ◽  
Social Domain ◽  
Network Information ◽  
Cross Domain ◽  
Real World Datasets

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

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.

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.

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