Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson… - Proceedings of the sixth …, 2012 - dl.acm.org
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
被引用次数：434 相关文章
[PDF] psu.edu

[PDF][PDF] CLiMF: Collaborative Less-Is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - Citeseer
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] cuhk.edu.hk

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - cse.cuhk.edu.hk
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] researchgate.net

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - researchgate.net
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] wordpress.com

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - alexiskz.wordpress.com
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] machinelearning.ru

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - machinelearning.ru
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …

CLiMF: collaborative less-is-more filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson… - Proceedings of the …, 2013 - dl.acm.org
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] machinelearning.ru

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - machinelearning.ru
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] academia.edu

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - academia.edu
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …
[PDF] archive.org

[PDF][PDF] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Y Shi, A Karatzoglou, L Baltrunas, M Larson, N Oliver… - 2012 - scholar.archive.org
In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …