Heterogeneous Side Information-based Iterative Guidance Model for Recommendation Article Swipe
Feifei Dai
,
Xiaoyan Gu
,
Zhuo Wang
,
Mingda Qian
,
Bo Li
,
Weiping Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3460426.3463631
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3460426.3463631
Heterogeneous side information has been widely used in recommender systems to alleviate the data sparsity problem. However, the heterogeneous side information in existing methods provides insufficient guidance for predicting user preferences as its effect is inevitably weakened during utilization. Furthermore, most existing methods cannot effectively utilize the heterogeneous side information to understand users and items. They often neglect the interrelation among various types of heterogeneous side information of a user or an item. As a result, it is difficult for existing methods to comprehensively understand users and items so that the recommender system recommends inappropriate items to users. To overcome the above drawbacks, we propose an interrelation learning-based recommendation method with iterative heterogeneous side information guidance (ILIG). ILIG includes two modules: 1) Iterative Heterogeneous Side Information Guidance Module. It uses heterogeneous side information to iteratively guide the prediction of user preferences, which effectively enhances the effect of the heterogeneous side information. 2) Interrelation Learning-based Portrait Construction Module. It captures the interrelation among various types of heterogeneous side information to comprehensively learn the representations of users and items. To demonstrate the effectiveness of ILIG, we conduct extensive experiments on Movielens-100K, Movielens-1M, and BookCrossing datasets. The experimental results show that ILIG outperforms the state-of-the-art recommender systems.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3460426.3463631
- https://dl.acm.org/doi/pdf/10.1145/3460426.3463631
- OA Status
- gold
- Cited By
- 6
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3197420363
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3197420363Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3460426.3463631Digital Object Identifier
- Title
-
Heterogeneous Side Information-based Iterative Guidance Model for RecommendationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-24Full publication date if available
- Authors
-
Feifei Dai, Xiaoyan Gu, Zhuo Wang, Mingda Qian, Bo Li, Weiping WangList of authors in order
- Landing page
-
https://doi.org/10.1145/3460426.3463631Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3460426.3463631Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://dl.acm.org/doi/pdf/10.1145/3460426.3463631Direct OA link when available
- Concepts
-
MovieLens, Computer science, Recommender system, Side effect (computer science), Information retrieval, Collaborative filtering, Data mining, Machine learning, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.so | 88 |
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| abstract_inverted_index.we | 103, 183 |
| abstract_inverted_index.The | 193 |
| abstract_inverted_index.and | 53, 86, 175, 190 |
| abstract_inverted_index.for | 27, 79 |
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| abstract_inverted_index.its | 32 |
| abstract_inverted_index.the | 12, 17, 46, 58, 90, 100, 136, 144, 147, 159, 171, 179, 200 |
| abstract_inverted_index.two | 119 |
| abstract_inverted_index.ILIG | 117, 198 |
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| abstract_inverted_index.that | 89, 197 |
| abstract_inverted_index.used | 6 |
| abstract_inverted_index.user | 29, 69, 139 |
| abstract_inverted_index.uses | 129 |
| abstract_inverted_index.with | 110 |
| abstract_inverted_index.ILIG, | 182 |
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| abstract_inverted_index.among | 60, 161 |
| abstract_inverted_index.guide | 135 |
| abstract_inverted_index.item. | 72 |
| abstract_inverted_index.items | 87, 95 |
| abstract_inverted_index.learn | 170 |
| abstract_inverted_index.often | 56 |
| abstract_inverted_index.types | 62, 163 |
| abstract_inverted_index.users | 52, 85, 174 |
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| abstract_inverted_index.cannot | 43 |
| abstract_inverted_index.during | 37 |
| abstract_inverted_index.effect | 33, 145 |
| abstract_inverted_index.items. | 54, 176 |
| abstract_inverted_index.method | 109 |
| abstract_inverted_index.system | 92 |
| abstract_inverted_index.users. | 97 |
| abstract_inverted_index.widely | 5 |
| abstract_inverted_index.(ILIG). | 116 |
| abstract_inverted_index.Module. | 127, 156 |
| abstract_inverted_index.conduct | 184 |
| abstract_inverted_index.methods | 23, 42, 81 |
| abstract_inverted_index.neglect | 57 |
| abstract_inverted_index.propose | 104 |
| abstract_inverted_index.result, | 75 |
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| abstract_inverted_index.systems | 9 |
| abstract_inverted_index.utilize | 45 |
| abstract_inverted_index.various | 61, 162 |
| abstract_inverted_index.Guidance | 126 |
| abstract_inverted_index.However, | 16 |
| abstract_inverted_index.Portrait | 154 |
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| abstract_inverted_index.enhances | 143 |
| abstract_inverted_index.existing | 22, 41, 80 |
| abstract_inverted_index.guidance | 26, 115 |
| abstract_inverted_index.includes | 118 |
| abstract_inverted_index.modules: | 120 |
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| abstract_inverted_index.problem. | 15 |
| abstract_inverted_index.provides | 24 |
| abstract_inverted_index.sparsity | 14 |
| abstract_inverted_index.systems. | 203 |
| abstract_inverted_index.weakened | 36 |
| abstract_inverted_index.Iterative | 122 |
| abstract_inverted_index.alleviate | 11 |
| abstract_inverted_index.datasets. | 192 |
| abstract_inverted_index.difficult | 78 |
| abstract_inverted_index.extensive | 185 |
| abstract_inverted_index.iterative | 111 |
| abstract_inverted_index.drawbacks, | 102 |
| abstract_inverted_index.inevitably | 35 |
| abstract_inverted_index.predicting | 28 |
| abstract_inverted_index.prediction | 137 |
| abstract_inverted_index.recommends | 93 |
| abstract_inverted_index.understand | 51, 84 |
| abstract_inverted_index.Information | 125 |
| abstract_inverted_index.demonstrate | 178 |
| abstract_inverted_index.effectively | 44, 142 |
| abstract_inverted_index.experiments | 186 |
| abstract_inverted_index.information | 2, 20, 49, 66, 114, 132, 167 |
| abstract_inverted_index.iteratively | 134 |
| abstract_inverted_index.outperforms | 199 |
| abstract_inverted_index.preferences | 30 |
| abstract_inverted_index.recommender | 8, 91, 202 |
| abstract_inverted_index.BookCrossing | 191 |
| abstract_inverted_index.Construction | 155 |
| abstract_inverted_index.Furthermore, | 39 |
| abstract_inverted_index.experimental | 194 |
| abstract_inverted_index.information. | 150 |
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| abstract_inverted_index.preferences, | 140 |
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| abstract_inverted_index.Interrelation | 152 |
| abstract_inverted_index.Movielens-1M, | 189 |
| abstract_inverted_index.effectiveness | 180 |
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| abstract_inverted_index.inappropriate | 94 |
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| abstract_inverted_index.Learning-based | 153 |
| abstract_inverted_index.learning-based | 107 |
| abstract_inverted_index.recommendation | 108 |
| abstract_inverted_index.Movielens-100K, | 188 |
| abstract_inverted_index.comprehensively | 83, 169 |
| abstract_inverted_index.representations | 172 |
| abstract_inverted_index.state-of-the-art | 201 |
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| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
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| citation_normalized_percentile.is_in_top_10_percent | False |