A hybrid recommendation system considering visual information for predicting favorite restaurants

Chu, Wei-Ta; Tsai, Ya-Lun

doi:10.1007/s11280-017-0437-1

Cited by 114 publications

(55 citation statements)

References 26 publications

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“…These representations are ultimately incorporated into the matrix factorization model for better recommendations. Chu and Tsai (2017) proposed EnFM, which extracts important words from textual reviews via term frequency and inverse document frequency (TF-IDF) technique (Ramos et al 2003). They enhanced the factorization machine (FM) by fusing the extracted words as features of users and items.…”

Section: Lfms+ffs Early Lfms (Seementioning

confidence: 99%

Research commentary on recommendations with side information: A survey and research directions

Sun

Guo

Yang

et al. 2019

Electronic Commerce Research and Applications

181

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Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.

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Section: Lfms+ffs Early Lfms (Seementioning

confidence: 99%

Research commentary on recommendations with side information: A survey and research directions

Sun

Guo

Yang

et al. 2019

Electronic Commerce Research and Applications

181

View full text Add to dashboard Cite

show abstract

“…Recently, Markus et al [46] used different kinds of features from different modalities, including a recipe's title, ingredient list and cooking directions, popularity indicators (e.g., the number of ratings) and visual features to estimate the healthiness of recipes for recipe recommendation. In addition, Chu et al [47] combined text information, metadata and visual features for restaurant attributes and user preference representation for restaurant recommendation. Recently, the capability of Deep Learning (DL) in processing heterogeneous data [48], [49] brings more opportunities in recommending more accurate and diverse items.…”

Section: Multimodal Food Analysis For Food Recommendationmentioning

confidence: 99%

Food Recommendation: Framework, Existing Solutions, and Challenges

Min

Jiang

Jain

2020

IEEE Trans. Multimedia

100

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A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field.

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“…Visual descriptors have also been used in restaurant recommendation systems by the authors of Chu and Tsai (2017), in which images collected from a restaurant-based social platform were first processed by an SVM-based image classification system that used both low-level and deep features and split the images into four classes, indoor, outdoor, food and drink images, based on the idea that these different categories of pictures may have different influences on restaurant recommendation. This content-based approach was used to successfully enhance the performance of matrix factorization, Bayesian personalized ranking matrix factorization and FM approaches.…”

Section: Image Recommendationmentioning

confidence: 99%

Movie genome: alleviating new item cold start in movie recommendation

Deldjoo

Dacrema

Constantin

et al. 2019

User Model User-Adap Inter

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As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a scenario, since the newly added videos lack interactions-a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual metadata. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automatically extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et alto better exploit complementary information between different modalities; (iii) proposing a two-step hybrid approach which trains a CF model on warm items (items with interactions) and leverages the learned model on the movie genome to recommend cold items (items without interactions). Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a user-centric online experiment measuring different subjective aspects, such as satisfaction and diversity. Results show the benefits of this approach compared to existing approaches.

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A hybrid recommendation system considering visual information for predicting favorite restaurants

Cited by 114 publications

References 26 publications

Research commentary on recommendations with side information: A survey and research directions

Research commentary on recommendations with side information: A survey and research directions

Food Recommendation: Framework, Existing Solutions, and Challenges

Movie genome: alleviating new item cold start in movie recommendation

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