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Tsuyoshi Murata

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3 papers

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AAAI Conference 2022 Conference Paper

Leaping through Time with Gradient-Based Adaptation for Recommendation

Nuttapong Chairatanakul
Hoang NT
Xin Liu
Tsuyoshi Murata

Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of the main challenges in recommender system modeling. Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based metalearning to model time dependencies. LeapRec characterizes temporal dynamics by two complement components named global time leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term patterns by finding the shortest learning path across unordered temporal data. Cooperatively, OTL learns short-term patterns by considering the sequential nature of the temporal data. Our experimental results show that LeapRec consistently outperforms the state-of-the-art methods on several datasets and recommendation metrics. Furthermore, we provide an empirical study of the interaction between GTL and OTL, showing the effects of long- and short-term modeling.

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AAAI Conference 1996 Conference Paper

Machine Discovery Based on Numerical Data Generated in Computer Experiments

Tsuyoshi Murata

In the discovery of useful theorems or formulas, experimental data acquisition plays a fundamental role. Most of the previous discovery systems which have the abilities for experimentation, however, require much knowledge for evaluating experimental results, or require plans of common experiments which are given to the systems in advance. Only few systems have been attempted to make experiments which enable the discovery based on acquired experimental data without depending on given initial knowledge. This paper proposes a new approach for discovering useful theorems in the domain of plane geometry by employing experimentation. In this domain, drawing a figure and observing it correspond to making experimentation since these two processes are preparations for acquiring geometrical data. EXPEDITION, a discovery system based on experimental data acquisition, generates figures by itself and acquires expressions describing relations among line segments and angles in the figures. Such expressions can be extracted from the numerical data obtained in the computer experiments. By using simple heuristics for drawing and observing figures, the system succeeds in discovering many new useful theorems and formulas as well as rediscovering well-known theorems, such as power theorems and Thales’ theorem.

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AAAI Conference 1994 Conference Paper

A Discovery System for Trigonometric Functions

Tsuyoshi Murata

This paper describes a discovery system for trigonometric fuctions (DST), which has abilities to acquire new knowledge in the form of theorcins ancl foriiialas ii1 a plant: geometry cloiiiaiil. The systcn1 is composed of two suhystenls: a plaiic: geonhry analysis systeiri ad a niatlicniatical fornda t, ratnsforlllat, ioll system. The former changes the length and angles of a figure and extracts geouietric relations, and the lat, tcr transforiiis the relations to acquire nsefnl formnlas. With little lmsic: knowledge such as the clefinitioii of the congruence of triangles aid the dcfiiiition of fiuidameiit~al trigonometric fiuictions, our system has recliscovered many trigononietric formulas ant1 geometric theorems, including the Pythagorean tlieoreui.

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