Numerical study of reciprocal recommendation with domain matching

Sudo, Kotaro; Osugi, Naoya; Kanamori, Takafumi

doi:10.1007/s42081-019-00033-3

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…An exploratory analysis on the Speed-Dating Experiment dataset 3 , shows that a reasonable trade-off between optimizing utilities and recommender performance is achieved. In [148], graph embedding is utilized for mapping feature vectors from multiple data sources into a common representation space. Lastly, the COUPLENET deep learning model [151] bets on recommending potential couples based on text data in widespread social media platforms e.g.…”

Section: Rodríguez Et Al [139]mentioning

confidence: 99%

“…Other works have considered simpler fusion strategies such as a sum or product of unidirectional preferences [13,153], whereas other have used logical connectives [89], set intersection between both users' recommendation lists [161] or aggregation of the ranking positions of x and y in each other's recommendation lists [107], to name a few. Some challenges and areas for Harmonic mean combined with sum Sudo et al [148] Sum of similarities/distances Almalis et al [13], Yu et al [166] Product operator Ting et al [153], Li and Li [96] Weighted mean Kleinermann et al [84], Xia et al [158] Multiple averaging and uninorm aggregation functions Neve and Palomares [112,113] Matrix multiplication Jacobsen and Spanakis [75] Set intersection of recommendable users Yacef and McLaren [161], Kutty et al [90] Aggregation (union) of probabilities Pizzato and Silvertrini [125] Average similarity between x and previous successful interactions with y…”

Section: Perspective A: Fusion Strategies and Reciprocitymentioning

confidence: 99%

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Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

Palomares¹,

Porcel²,

Pizzato³

et al. 2020

Preprint

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Many social services including online dating, social media, recruitment and online learning, largely rely on "matching people with the right people". The success of these services and the user experience with them often depends on their ability to match users. Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendation services, since they need to take into account both users' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

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Section: Rodríguez Et Al [139]mentioning

confidence: 99%

Section: Perspective A: Fusion Strategies and Reciprocitymentioning

confidence: 99%

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

Palomares¹,

Porcel²,

Pizzato³

et al. 2020

Preprint

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show abstract

“…There are several criteria used to measure the prediction accuracy. Here, we use the mean average precision (MAP), because the MAP is a typical metric for evaluating the performance of recommender systems; see [5,[50][51][52][53][54][55][56] and references therein for more details.…”

Section: Numerical Experiments Of Reciprocal Recommendationmentioning

confidence: 99%

“…We used 130,8126 messages from 3 January 2016 to 31 October 2016 as the training data. Test data consists of 177,450 messages from 1 November 2016 to 5 June 2017 [55]. The proportion of edges in the test set to all data set is approximately 0.12.…”

Section: Real-world Datamentioning

confidence: 99%

Model Description of Similarity-Based Recommendation Systems

Kanamori

Osugi²

2019

Entropy

Self Cite

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The quality of online services highly depends on the accuracy of the recommendations they can provide to users. Researchers have proposed various similarity measures based on the assumption that similar people like or dislike similar items or people, in order to improve the accuracy of their services. Additionally, statistical models, such as the stochastic block models, have been used to understand network structures. In this paper, we discuss the relationship between similarity-based methods and statistical models using the Bernoulli mixture models and the expectation-maximization (EM) algorithm. The Bernoulli mixture model naturally leads to a completely positive matrix as the similarity matrix. We prove that most of the commonly used similarity measures yield completely positive matrices as the similarity matrix. Based on this relationship, we propose an algorithm to transform the similarity matrix to the Bernoulli mixture model. Such a correspondence provides a statistical interpretation to similarity-based methods. Using this algorithm, we conduct numerical experiments using synthetic data and real-world data provided from an online dating site, and report the efficiency of the recommendation system based on the Bernoulli mixture models.

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“…This special feature comprises three contributions of scientists working actively in the area. Sudo et al (2019) are concerned with reciprocal recommendation, which has attracted great attention. They propose a new method of reciprocal recommendation by using a graph-embedding technique called cross-domain matching correlation (CDMCA) (Shimodaira 2015).…”

mentioning

confidence: 99%