Collaborative filtering for mobile application recommendation with implicit feedback

This paper introduces a novel dataset regarding the installation of mobile applications in users devices, and benchmarks multiple well-established collaborative filtering techniques, leveraging on the user implicit feedback extracted from the data. Our experiments use 3 snapshots provided by Aptoide, one of the leading mobile application stores. These snapshots provide information about the installed applications for more than 4 million users in total. Such data allow us to infer the users activity over time, which corresponds to an implicit measure of interest in a certain application, as we consider that installs reflect a positive user opinion on an app, and, inversely, uninstalls reflect a negative user opinion. Since recommendation systems usually use explicit rating data, we have filtered and transformed the existing data into binary ratings. We have trained several recommendation models, using the Surprise Python scikit, comparing baseline algorithms to neighborhood-based and matrix factorization methods. Our evaluation shows that SVD-based and KNN-based methods achieve good performance scores while being computationally efficient, suggesting that they are suitable for recommendation in this novel dataset.info:eu-repo/semantics/acceptedVersio

A general aspect-term-extraction model for multi-criteria recommendations

In recent years, increasingly large quantities of user reviews have been made available by several e-commerce platforms. This content is very useful for recommender systems (RSs), since it reflects the users' opinion of the items regarding several aspects. In fact, they are especially valuable for RSs that are able to exploit multi-faceted user ratings. However, extracting aspect-based ratings from unstructured text is not a trivial task. Deep Learning models for aspect extraction have proven to be effective, but they need to be trained on large quantities of domain-specific data, which are not always available. In this paper, we explore the possibility of transferring knowledge across domains for automatically extracting aspects from user reviews, and its implications in terms of recommendation accuracy. We performed different experiments with several Deep Learning-based Aspect Term Extraction (ATE) techniques and Multi-Criteria recommendation algorithms. Results show that our framework is able to improve recommendation accuracy compared to several baselines based on single-criteria recommendation, despite the fact that no labeled data in the target domain was used when training the ATE model

Utilizing Review Summarization in a Spoken Recommendation System

In this paper we present a framework for spoken recommendation systems. To provide reliable recommendations to users, we incorporate a review summarization technique which extracts informative opinion summaries from grass-roots users‘ reviews. The dialogue system then utilizes these review summaries to support both quality-based opinion inquiry and feature- specific entity search. We propose a probabilistic language generation approach to automatically creating recommendations in spoken natural language from the text-based opinion summaries. A user study in the restaurant domain shows that the proposed approaches can effectively generate reliable and helpful recommendations in human-computer conversations.T-Party ProjectQuanta Computer (Firm

NOTION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE - AN EMPIRICAL INVESTIGATION FROM A USER\u27S PERSPECTIVE

The growing attention on artificial intelligence-based decision-making has led to research interest in the explainability and interpretability of machine learning models, algorithmic transparency, and comprehensibility. This renewed attention on XAI advocates the need to investigate end user-centric explainable AI, due to the universal adoption of AI-based systems at the root level. Therefore, this paper investigates user-centric explainable AI from a recommendation systems context. We conducted focus group interviews to collect qualitative data on the recommendation system. We asked participants about the end users\u27 comprehension of a recommended item, its probable explanation and their opinion of making a recommendation explainable. Our finding reveals end users want a non-technical and tailor-made explanation with on-demand supplementary information. Moreover, we also observed users would like to have an explanation about personal data usage, detailed user feedback, authentic and reliable explanations. Finally, we proposed a synthesized framework that will include end users in the XAI development process

Enforcing recommendation in Social networks By user interests

It is a human tendency to get others opinion before doing something, this we can see much more in friends circle. As the internet spreads over the world it interferes much more in our daily life, for example applications like Facebook and whatsup becomes the inseparable part of internet savvy peoples. So it is obvious that most of the users search their friends based on their taste or keywords using the recommendation system. Many recommendation systems are existed which provides the recommendation by capturing users taste or profile data in the social networking site. This paper put forwards an idea of creating recommendation system based on the collected user comment data from the social networking site pages using an efficient web crawler. This method enhances to get the recommendation from many social networking sites in a given instance. This makes the system as an independent adaptive model which can be easily apply on many social networking sites to get user recommendation for the given query. System strongly empowered by the well grained NLP protocols with fuzzy classification approach. DOI: 10.17762/ijritcc2321-8169.15084

Personalized Expert Recommendation: Models and Algorithms

Many large-scale information sharing systems including social media systems, questionanswering sites and rating and reviewing applications have been growing rapidly, allowing millions of human participants to generate and consume information on an unprecedented scale. To manage the sheer growth of information generation, there comes the need to enable personalization of information resources for users — to surface high-quality content and feeds, to provide personally relevant suggestions, and so on. A fundamental task in creating and supporting user-centered personalization systems is to build rich user profile to aid recommendation for better user experience. Therefore, in this dissertation research, we propose models and algorithms to facilitate the creation of new crowd-powered personalized information sharing systems. Specifically, we first give a principled framework to enable personalization of resources so that information seekers can be matched with customized knowledgeable users based on their previous historical actions and contextual information; We then focus on creating rich user models that allows accurate and comprehensive modeling of user profiles for long tail users, including discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile. In particular, this dissertation research makes two unique contributions: First, we introduce the problem of personalized expert recommendation and propose the first principled framework for addressing this problem. To overcome the sparsity issue, we investigate the use of user’s contextual information that can be exploited to build robust models of personal expertise, study how spatial preference for personally-valuable expertise varies across regions, across topics and based on different underlying social communities, and integrate these different forms of preferences into a matrix factorization-based personalized expert recommender. Second, to support the personalized recommendation on experts, we focus on modeling and inferring user profiles in online information sharing systems. In order to tap the knowledge of most majority of users, we provide frameworks and algorithms to accurately and comprehensively create user models by discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile, with each described shortly as follows: —We develop a probabilistic model called Bayesian Contextual Poisson Factorization to discover what users are known for by others. Our model considers as input a small fraction of users whose known-for profiles are already known and the vast majority of users for whom we have little (or no) information, learns the implicit relationships between user?s known-for profiles and their contextual signals, and finally predict known-for profiles for those majority of users. —We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users, and demonstrate how user’s opinion bias can be exploited to recommend other users with similar opinion in social networks. — We study how a user’s topical profile varies geo-spatially and how we can model a user’s geo-spatial known-for profile as the last step in our dissertation for creation of rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating user contexts into the two-layered hierarchical user model for better representation of user’s geo-topic preference by others

Emergence of scale-free leadership structure in social recommender systems

The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems

ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user\u27s opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called cold-start issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation