Social Recommender

Online the idea of this paper is to give insights about a web-based project that aims to resolve the users query using machine learning technique of content-based filtering. The crux is to define the scope of the website. It becomes crucial to understand the business prospects of the project along with the relevance in the current technical world. As the paper goes about, various other papers and ideas will also be discussed that have been previously published and implemented. This paper will also break down the concept of content-based filtering for any individual who is a beginner in the field of machine learning.

Online the idea of this paper is to give insights about a web-based project that aims to resolve the users query using machine learning technique of content-based filtering. The crux is to define the scope of the website. It becomes crucial to understand the business prospects of the project along with the relevance in the current technical world. As the paper goes about, various other papers and ideas will also be discussed that have been previously published and implemented. This paper will also break down the concept of content-based filtering for any individual who is a beginner in the field of machine learning.

Online the idea of this paper is to give insights about a web-based project that aims to resolve the users query using machine learning technique of content-based filtering. The crux is to define the scope of the website. It becomes crucial to understand the business prospects of the project along with the relevance in the current technical world. As the paper goes about, various other papers and ideas will also be discussed that have been previously published and implemented. This paper will also break down the concept of content-based filtering for any individual who is a beginner in the field of machine learning.

Daily-deal applications are popular implementations of on-line advertising strategies that offer products and services to users based on their personal profiles. The current implementations are effective but can frustrate users with irrelevant deals due to stale profiles. To exploit these applications fully, deals must become smarter and context-aware. This paper presents SmarterDeals, our deal recommendation system that exploits users' changing personal context information to deliver highly relevant offers. SmarterDeals relies on recommendation algorithms based on collaborative filtering, and SmarterContext, our adaptive context management framework. SmarterContext provides SmarterDeals with up-to-date information about users' locations and product preferences gathered from their past and present web interactions. For many deal categories the accuracy of SmarterDeals is between 3% and 8% better than the approaches we used as baselines. For some categories, and in terms of m...

We test the impact of integrating a measure of common friendship in collaborative filtering, in order to capture the intuition that socially interconnected groups of people tend to have similar tastes. An experiment on the Yelp dataset shows that using preference information derived from the commonalities of interests in networks of friends achieves higher accuracy than item-to-item collaborative filtering.

The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of user interests, it seems reasonable to combine it with social networks information and the factor of time. Hence, this study, for the first time, tries to assess the efficiency of the recommender systems by combining fuzzy logic, longitudinal data and social networks information such as tags, friendship, and membership in groups. And the impact of the proposed algorithm for improving the accuracy of recommender systems was studied by specifying the neighborhood and the border between the users’ preferences over time. The results revealed that using longitudinal data in social networks informatio...

Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.

Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.

Nowadays a big challenge when going out to a new restaurant or cafe, people usually use websites or applications to look up nearby places and then choose one based on an average rating. But most of the time the average rating isn't enough to predict the quality or hygiene of the restaurant. Different people have different perspectives and priorities when evaluating a restaurant. Many online businesses now have implemented personalized recommendation systems which basically try to identify user preferences and then provide relevant products to enhance the users experience. In turn, users will be able to enjoy exploring what they might like with convenience and ease because of the recommendation results. Finding an ideal restaurant can be a struggle because the mainstream recommender apps have not yet adopted the personalized recommender approach. So we took up this challenge and we aim to build the prototype of a personalized recommender system that incorporates metadata which is basically the information provided by interactions of customers and restaurants online(reviews), which gives a pretty good idea of customers satisfaction and taste as well as features of the restaurant. This type of approach enhances user experience of finding a restaurant that suits their taste better. This paper has used a package called lightfm(the library of python for implementing popular recommendation algorithms) and the dataset from yelp. There are different methods of filtering the data, here we have used Hybrid filtering which is a combination of Content-based filtering (CBF) and Collaborative Filtering (CF). Since the results from Hybrid filtering are far more closer to accuracy than CBF or CF respectively. Then hybrid filtering gives results in the form of personalized recommendations for users after training and testing of the data

We test the impact of integrating a measure of {\em common friendship} in collaborative filtering, in order to capture the intuition that socially interconnected groups of people tend to have similar tastes. An experiment on the Yelp dataset shows that using preference information derived from the commonalities of interests in networks of friends achieves higher accuracy than item-to-item collaborative filtering.