A sentiment-based item description approach for kNN collaborative filtering

In this paper, we propose an approach based on sentiment analysis to describe items in a neighborhood-based collaborative filtering model. We use unstructured users' reviews to produce a vector-based representation that considers the overall sentiment of those reviews towards specific features. We propose and compare two different techniques to obtain and score such features from textual content, namely term-based and aspect-based feature extraction. Finally, our proposal is compared against structured metadata under the same recommendation algorithm, whose results show a significant improvement over the baselines.FAPESP (process numbers 2013/10756-5, and 2013/22547-1

Peeking into the other half of the glass : handling polarization in recommender systems.

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users\u27 interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item\u27s content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated with the same hashtag. However hashtags are not used in a consistent manner and most importantly, are completely optional to use. This makes them unreliable as the sole mechanism for searching for relevant information. We investigate mechanisms for consolidating the hashtag space using recommender systems. Our methods are general enough that they can be used for hashtag annotation in various social media services such as twitter, as well as for general item recommendations on systems that rely on implicit user interest data such as e-learning and news sites, or explicit user ratings, such as e-commerce and online entertainment sites. To conclude, we propose a methodology to extract stories based on two types of hashtag co-occurrence graphs. Our research in hashtag recommendation was able to exploit the textual content that is available as part of user messages or posts, and thus resulted in hybrid recommendation strategies. Using content within this context can bridge polarization boundaries. However, when content is not available, is missing, or is unreliable, as in the case of platforms that are rich in multimedia and multilingual posts, the content option becomes less powerful and pure collaborative filtering regains its important role, along with the challenges of polarization

An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis

In this paper, we propose a global architecture of a recommender tool, which represents a part of an existing collaborative platform. This tool provides diagnostic documents for industrial operators. The recommendation process considered here is composed of three steps: Collecting and filtering information; Prediction or recommendation step; evaluating and improvement. In this work, we focus on collecting and filtering step. We mainly use information result from collaborative sessions and documents describing solutions that are attributed to the complex diagnostic problems. The developed tool is based on collaborative filtering that operates on users' preferences and similar responses

A Review of Movie Recommendation System : Limitations, Survey and Challenges

Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored

Recommendation and Sentiment Analysis Based on Consumer Review and Rating

Accurate analysis and recommendation on products based on online reviews and rating data play an important role in precisely targeting suitable consumer segmentations and therefore can promote merchandise sales. This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs. Among them, the beer recommendation is based on rating data; 10 classification models are compared in text sentiment analysis, including the conventional machine learning models and deep learning models. Combining the two analyses can increase the credibility of the recommended beer and help increase beer sales. The experiment proves that this method can filter the products with more negative reviews in the recommendation algorithm and improve user acceptance

SENTIMENT ANALYSIS USING STRING TOKEN CLASSIFICATION ALGORITHM

Sentiment analysis is a type of data mining which involves computation of opinions, sentiments and to determine if an information or a piece of text conveys positive, negative or neutral opinion. Public opinion regarding various aspects can be found using sentiment analysis. Clustering and classification are the key techniques in sentiment analysis. Consensus clustering is better than existing clustering algorithms as it provides a stable and efficient final result. However, it has its own drawbacks. Instead of performing consensus clustering and selecting classifiers from the consolidated result, we try to develop a new classification algorithm in our wor

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty