Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Recommendation": models, code, and papers

BI-REC: Guided Data Analysis for Conversational Business Intelligence

May 02, 2021
Venkata Vamsikrishna Meduri, Abdul Quamar, Chuan Lei, Vasilis Efthymiou, Fatma Ozcan

Conversational interfaces to Business Intelligence (BI) applications enable data analysis using a natural language dialog in small incremental steps. To truly unleash the power of conversational BI to democratize access to data, a system needs to provide effective and continuous support for data analysis. In this paper, we propose BI-REC, a conversational recommendation system for BI applications to help users accomplish their data analysis tasks. We define the space of data analysis in terms of BI patterns, augmented with rich semantic information extracted from the OLAP cube definition, and use graph embeddings learned using GraphSAGE to create a compact representation of the analysis state. We propose a two-step approach to explore the search space for useful BI pattern recommendations. In the first step, we train a multi-class classifier using prior query logs to predict the next high-level actions in terms of a BI operation (e.g., {\em Drill-Down} or {\em Roll-up}) and a measure that the user is interested in. In the second step, the high-level actions are further refined into actual BI pattern recommendations using collaborative filtering. This two-step approach allows us to not only divide and conquer the huge search space, but also requires less training data. Our experimental evaluation shows that BI-REC achieves an accuracy of 83% for BI pattern recommendations and up to 2X speedup in latency of prediction compared to a state-of-the-art baseline. Our user study further shows that BI-REC provides recommendations with a [email protected] of 91.90% across several different analysis tasks.

* 16 pages, 16 figures, 4 tables 

  Access Paper or Ask Questions

Multi-faceted Trust-based Collaborative Filtering

Mar 25, 2020
Noemi Mauro, Liliana Ardissono, Zhongli Filippo Hu

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.

* Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2019) 

  Access Paper or Ask Questions

Inferring Complementary Products from Baskets and Browsing Sessions

Sep 25, 2018
Ilya Trofimov

Complementary products recommendation is an important problem in e-commerce. Such recommendations increase the average order price and the number of products in baskets. Complementary products are typically inferred from basket data. In this study, we propose the BB2vec model. The BB2vec model learns vector representations of products by analyzing jointly two types of data - Baskets and Browsing sessions (visiting web pages of products). These vector representations are used for making complementary products recommendation. The proposed model alleviates the cold start problem by delivering better recommendations for products having few or no purchases. We show that the BB2vec model has better performance than other models which use only basket data.

* Workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning (RecSysKTL'18) 

  Access Paper or Ask Questions

Bandits Under The Influence (Extended Version)

Sep 21, 2020
Silviu Maniu, Stratis Ioannidis, Bogdan Cautis

Recommender systems should adapt to user interests as the latter evolve. A prevalent cause for the evolution of user interests is the influence of their social circle. In general, when the interests are not known, online algorithms that explore the recommendation space while also exploiting observed preferences are preferable. We present online recommendation algorithms rooted in the linear multi-armed bandit literature. Our bandit algorithms are tailored precisely to recommendation scenarios where user interests evolve under social influence. In particular, we show that our adaptations of the classic LinREL and Thompson Sampling algorithms maintain the same asymptotic regret bounds as in the non-social case. We validate our approach experimentally using both synthetic and real datasets.

* 27 pages, 4 figures, 6 tables. Extended version of accepted ICDM 2020 conference article 

  Access Paper or Ask Questions

Concept-based Recommendations for Internet Advertisement

Jun 26, 2009
Dmitry I. Ignatov, Sergei O. Kuznetsov

The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the company. The goal of this work is to propose better interpretable recommendations based on FCA and association rules.

* D.I.Ignatov, S.O. Kuznetsov. Concept-based Recommendations for Internet Advertisement//In proceedings of The Sixth International Conference Concept Lattices and Their Applications (CLA'08), Olomouc, Czech Republic, 2008 ISBN 978-80-244-2111-7 

  Access Paper or Ask Questions

Information Cocoons in Online Navigation

Sep 14, 2021
Lei Hou, Xue Pan, Kecheng Liu, Zimo Yang, Jianguo Liu, Tao Zhou

Social media and online navigation bring us enjoyable experience in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information. We provide a formal definition of IC in the scenario of online navigation. Subsequently, by analyzing real recommendation networks extracted from Science, PNAS and Amazon websites, and testing mainstream algorithms in disparate recommender systems, we demonstrate that similarity-based recommendation techniques result in ICs, which suppress the system navigability by hundreds of times. We further propose a flexible recommendation strategy that solves the IC-induced problem and improves retrieval accuracy in navigation, demonstrated by simulations on real data and online experiments on the largest video website in China.

* 14 pages, 3 figures, 1 table, 30 references 

  Access Paper or Ask Questions

Attention-based Fusion for Outfit Recommendation

Aug 28, 2019
Katrien Laenen, Marie-Francine Moens

This paper describes an attention-based fusion method for outfit recommendation which fuses the information in the product image and description to capture the most important, fine-grained product features into the item representation. We experiment with different kinds of attention mechanisms and demonstrate that the attention-based fusion improves item understanding. We outperform state-of-the-art outfit recommendation results on three benchmark datasets.

* 6 pages 

  Access Paper or Ask Questions

Dataset Definition Standard (DDS)

Jan 07, 2021
Cyril Cappi, Camille Chapdelaine, Laurent Gardes, Eric Jenn, Baptiste Lefevre, Sylvaine Picard, Thomas Soumarmon

This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This document is one of the 3 documents defined in [1] to ensure the quality of datasets. This is a work in progress as good practices evolve along with our understanding of machine learning. The document is divided into three main parts. Section 2 addresses the data collection activity. Section 3 gives recommendations about the annotation process. Finally, Section 4 gives recommendations concerning the breakdown between train, validation, and test datasets. In each part, we first define the desired properties at stake, then we explain the objectives targeted to meet the properties, finally we state the recommendations to reach these objectives.

  Access Paper or Ask Questions

Negative Sampling for Recommendation

Apr 02, 2022
Bin Liu, Bang Wang

How to effectively sample high-quality negative instances is important for well training a recommendation model. We argue that a high-quality negative should be both \textit{informativeness} and \textit{unbiasedness}. Although previous studies have proposed some approaches to address the informativeness in negative sampling, few has been done to discriminating false negative from true negative for unbiased negative sampling, not to mention taking both into consideration. This paper first adopts a parameter learning perspective to analyze negative informativeness and unbiasedness in loss gradient-based model training. We argue that both negative sampling and collaborative filtering include an implicit task of negative classification, from which we report an insightful yet beneficial finding about the order relation in predicted negatives' scores. Based on our finding and by regarding negatives as random variables, we next derive the class condition density of true negatives and that of false negatives. We also design a Bayesian classifier for negative classification, from which we define a quantitative unbiasedness measure for negatives. Finally, we propose to use a harmonic mean of informativeness and unbiasedness to sample high-quality negatives. Experimental studies validate the superiority of our negative sampling algorithm over the peers in terms of better sampling quality and better recommendation performance.

* Bayesian Negative Sampling for Recommendation 

  Access Paper or Ask Questions

Using consumer behavior data to reduce energy consumption in smart homes

Oct 01, 2015
Daniel Schweizer, Michael Zehnder, Holger Wache, Hans-Friedrich Witschel, Danilo Zanatta, Miguel Rodriguez

This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to existing algorithms regarding completeness/correctness of the results, run times as well as memory consumption and elaborates on the shortcomings of the different solutions. We also present a recommender system based on the developed algorithm that provides recommendations to the users to reduce their energy consumption. The recommender system was deployed to a set of test homes. The test participants rated the impact of the recommendations on their comfort. We used this feedback to adjust the system parameters and make it more accurate during a second test phase.

* To be presented at IEEE International Conference of Machine Learning and Applications (ICMLA, Dec. 2015). arXiv admin note: text overlap with arXiv:1509.05722 

  Access Paper or Ask Questions