Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Togashi, Riku"'
We introduce a layout similarity measure designed to evaluate the results of layout generation. While several similarity measures have been proposed in prior research, there has been a lack of comprehensive discussion about their behaviors. Our resea
Externí odkaz:
http://arxiv.org/abs/2407.12356
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has been a growing understanding t
Externí odkaz:
http://arxiv.org/abs/2402.14369
In matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform. Unlike standard recommender systems that suggest items to users, reciprocal recommender systems (RRSs) t
Externí odkaz:
http://arxiv.org/abs/2306.09060
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance
Externí odkaz:
http://arxiv.org/abs/2306.05292
Autor:
Ohsaka, Naoto, Togashi, Riku
Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity obje
Externí odkaz:
http://arxiv.org/abs/2305.13801
Autor:
Ohsaka, Naoto, Togashi, Riku
Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems are partly due to low-dimensionality of user and i
Externí odkaz:
http://arxiv.org/abs/2305.13597
Autor:
Otani, Mayu, Togashi, Riku, Sawai, Yu, Ishigami, Ryosuke, Nakashima, Yuta, Rahtu, Esa, Heikkilä, Janne, Satoh, Shin'ichi
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. However, our survey of 37 recent papers reveals that many works rely sole
Externí odkaz:
http://arxiv.org/abs/2304.01816
Autor:
Togashi, Riku, Abe, Kenshi
Recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue in practical systems. The notion of item fairness requires controlling the
Externí odkaz:
http://arxiv.org/abs/2209.04394
Publikováno v:
in IEEE Access, vol. 10, pp. 120023-120034, 2022
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combi
Externí odkaz:
http://arxiv.org/abs/2209.03126
Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users. We discuss the potential interactions between reciprocal recommender s
Externí odkaz:
http://arxiv.org/abs/2208.11384