An Efficient Similarity Measure for Collaborative Filtering

Mu, Yi; Xiao, Nianhao; Tang, Ruichun; Luo, Liang; Yin, Xiaobin

doi:10.1016/j.procs.2019.01.258

Cited by 20 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They proposed a parameterized similarity measure that considers the distance between users, common ratings, and uncommon ratings. Mu et al [19] used Hellinger distance to mitigate the effect of the sparsity problem on the similarity computation. This measure is used for finding a global similarity measure, and hence a weighted sum of local and global similarities is used for predictions.…”

Section: Related Workmentioning

confidence: 99%

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Al-Shamri

2022

Preprint

View full text Add to dashboard Cite

Similarity measures are crucial for electing neighbors for the users of recommender systems. However, the massive amount of the processed data in such applications may hide the inner nature of the utilized similarity measures. This paper devotes lots of studies to three standard similarity measures based on many synthetic and real datasets. The aim is to uncover the hidden nature of such measures and conclude their suitability for the recommender systems under different scenarios. Moreover, we propose a novel similarity measure called the normalized sum of multiplications (NSM) and two different variants of it. For experimentation, we examine all measures at three levels; a toy example, synthetic datasets, and real-world datasets. The results show that sometimes Pearson correlation coefficient and cosine similarity exclude similar neighbors in favor of less valuable ones. The former measures the correlation direction, while the latter measures the angle. However, both direction and angle are not similarity but an indication to it and can have the same values for two far vectors. On the other hand, the proposed similarity measure constantly reveals the exact similarity and tracks the closest neighbors. The results prove its robustness and its very good predictive accuracy compared to the traditional ones.

show abstract

Section: Related Workmentioning

confidence: 99%

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Al-Shamri

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A value is a common value if it occurs no less than a specified number of times, i.e., the corresponding threshold in θ rare = θ etype , θ act , θ host , θ part , θ loc , θ perio ; otherwise, it is a rare value. A common value's importance is evaluated by its frequency in important events, whereas a rare value's importance is estimated using hidden correlation [46,47], as defined below.…”

Section: Referencing Solutions Of Similar Cases (Rssc)mentioning

confidence: 99%

“…Given a rare value, common values under the same attribute are alternative values. The extent to which an alternative value matches the event is assessed by the transition probability (TP) [46]:…”

Section: Referencing Solutions Of Similar Cases (Rssc)mentioning

confidence: 99%

Improving Temporal Event Scheduling through STEP Perpetual Learning

et al. 2022

View full text Add to dashboard Cite

Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods.

show abstract

“…Collaborative filtering has also drawbacks of poor versatility and sparse data. (41) Considering the above facts, Mu (42) proposed an efficient similarity measure for collaborative filtering. To address versatility, a local similarity measure was proposed for sparse problems, the global users' similarity was estimated by computing the Hellinger Distance and Jaccard value of all ratings.…”

Section: Developmentmentioning

confidence: 99%

A Review of Data and Document Clustering pertaining to various Distance Measures

Sumathi

Grace

2022

Salud, Ciencia y Tecnología

View full text Add to dashboard Cite

Data is being generated at an increasing rate in a variety of fields as science and technology advance. The generated data are being saved for future decision-making. Data mining is the process of extracting patterns and useful information from massive amounts of data. The distance measure, which is used to calculate how different two objects are from one another, is one such instrument. We have conducted a comprehensive survey of how the distance measures behave when employed with different algorithms. Furthermore, the effectiveness and performance of some novel similarity measures proposed by other authors are investigated.

show abstract

An Efficient Similarity Measure for Collaborative Filtering

Cited by 20 publications

References 4 publications

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Improving Temporal Event Scheduling through STEP Perpetual Learning

A Review of Data and Document Clustering pertaining to various Distance Measures

Contact Info

Product

Resources

About