Time weight collaborative filtering

Ding, Yi; Li, Xue

doi:10.1145/1099554.1099689

Cited by 375 publications

(244 citation statements)

References 18 publications

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“…In this paper, to compute this temporal weight, we adopt a timeexponential function [7] that weights a given item according to its publication date.…”

Section: A Time-aware Methods Calculation 1) Structuro-temporal Weighmentioning

confidence: 99%

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Time-Aware Egocentric Network-Based User Profiling

On-At¹,

Canut²,

Péninou³

et al. 2017

Encyclopedia of Social Network Analysis and Mining

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-Improving the egocentric network-based user's profile building process by taking into account the dynamic characteristics of social networks can be relevant in many applications. To achieve this aim, we propose to apply a timeaware method into an existing egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's egocentric network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic egocentric network-based user profiling process.

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“…In this paper, to compute this temporal weight, we adopt a timeexponential function [7] that weights a given item according to its publication date.…”

Section: A Time-aware Methods Calculation 1) Structuro-temporal Weighmentioning

confidence: 99%

“…This approach proposes to assign the greater value to recent information [6]. For this, the exponential time function is widely used to weight information by gradually decreasing the weight of the older one [7].…”

Section: Techniquementioning

confidence: 99%

Time-Aware Egocentric Network-Based User Profiling

On-At¹,

Canut²,

Péninou³

et al. 2017

Encyclopedia of Social Network Analysis and Mining

View full text Add to dashboard Cite

show abstract

“…[21] presents a graph-based recommendation system that mixes long-term and short-term user preferences to improve predictions accuracy, while [20] considers how time can be used into matrix factorization models by examining changes in user and society tastes and habits, and items popularity. [9] uses a strategy, similar to our damped window model, that decreases the importance of known ratings as time distance from recommendation time increases. However, the proposed algorithm uses clustering to discriminate between different kinds of items.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Time-Aware Recommendations

Stefanidis

Ntoutsi

Nørvåg

et al. 2012

Lecture Notes in Computer Science

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Abstract. Recently, recommendation systems have received significant attention. However, most existing approaches focus on recommending items of potential interest to users, without taking into consideration how temporal information influences the recommendations. In this paper, we argue that time-aware recommendations need to be pushed in the foreground. We introduce an extensive model for time-aware recommendations from two perspectives. From a fresh-based perspective, we propose using a suite of aging schemes towards making recommendations mostly depend on fresh and novel user preferences. From a context-based perspective, we focus on providing different suggestions under different temporal specifications. The proposed strategies are experimentally evaluated using real movies ratings.

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“…The idea of making language models adaptive by introducing a decay function has appeared in various contexts such as speech recognition [22], news retrieval [23], email clustering [24], and collaborative filtering [25]. However, to the best of our knowledge, the effective behaviour and efficient implementation of timesensitive language modelling for the problem of online term recurrence prediction have not been studied before.…”

Section: Related Workmentioning

confidence: 99%

Time-Sensitive Language Modelling for Online Term Recurrence Prediction

Zhang

Mao

et al. 2009

Lecture Notes in Computer Science

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Abstract.We address the problem of online term recurrence prediction: for a stream of terms, at each time point predict what term is going to recur next in the stream given the term occurrence history so far. It has many applications, for example, in Web search and social tagging. In this paper, we propose a time-sensitive language modelling approach to this problem that effectively combines term frequency and term recency information, and describe how this approach can be implemented efficiently by an online learning algorithm. Our experiments on a real-world Web query log dataset show significant improvements over standard language modelling.

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Time weight collaborative filtering

Cited by 375 publications

References 18 publications

Time-Aware Egocentric Network-Based User Profiling

Time-Aware Egocentric Network-Based User Profiling

A Framework for Time-Aware Recommendations

Time-Sensitive Language Modelling for Online Term Recurrence Prediction

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