498 research outputs found
Explainable Neural Attention Recommender Systems
Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented variety of available items, making generated suggestions irrelevant to many users. Furthermore, many systems have been recently expected to accompany their suggestions with corresponding reasoning. Users who receive unjustified recommendations they do not agree with are susceptible to stop using the system or ignore its suggestions. In this work we investigate the current trends in the field of recommender systems and focus on two rising areas, deep recommendation and explainable recommender systems. First we present Textual and Contextual Embedding-based Neural Recommender (TCENR), a model that mitigates the data sparsity problem in the area of point-of-interest (POI) recommendation. This method employs different types of deep neural networks to learn varied perspectives of the same user-location interaction, using textual reviews, geographical data and social networks
What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
Purpose:
The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.
Design/methodology/approach:
A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel NaĂŻve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.
Findings:
The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.
Research limitations/implications:
The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.
Originality/value:
Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective
Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval
Neural networks with deep architectures have demonstrated significant
performance improvements in computer vision, speech recognition, and natural
language processing. The challenges in information retrieval (IR), however, are
different from these other application areas. A common form of IR involves
ranking of documents--or short passages--in response to keyword-based queries.
Effective IR systems must deal with query-document vocabulary mismatch problem,
by modeling relationships between different query and document terms and how
they indicate relevance. Models should also consider lexical matches when the
query contains rare terms--such as a person's name or a product model
number--not seen during training, and to avoid retrieving semantically related
but irrelevant results. In many real-life IR tasks, the retrieval involves
extremely large collections--such as the document index of a commercial Web
search engine--containing billions of documents. Efficient IR methods should
take advantage of specialized IR data structures, such as inverted index, to
efficiently retrieve from large collections. Given an information need, the IR
system also mediates how much exposure an information artifact receives by
deciding whether it should be displayed, and where it should be positioned,
among other results. Exposure-aware IR systems may optimize for additional
objectives, besides relevance, such as parity of exposure for retrieved items
and content publishers. In this thesis, we present novel neural architectures
and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020
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