Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey
Abstract
:1. Introduction
- This study offers a novel explanation and insight into the detailed process of reaching and extracting the auxiliary information needed for cold start recommendation.
- This study categorizes and presents a taxonomy of the approaches used for eliciting auxiliary information needed for cold start recommendation.
- This study shows and identifies several challenges associated with eliciting auxiliary information for a cold start recommendation. One of the most noticeable challenges from the results is that deriving the auxiliary side information usually involves two distinct processes.
- This study illustrates the three (3) main processes or strategies used in obtaining auxiliary information by adapting traditional filtering and matrix factorization algorithms, typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendation in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations.
- This study presents the bigger picture of research productivity and outcomes in cold start recommender systems. This study identified the need of key areas that cold start recommendation research is yet weak especially with the use of deep learning approaches, so that researchers and recommender system practitioners could further understand the technicality of recommendation to a new user and serve as motivation in pursuing further investigations.
2. Methodology
2.1. Literature Search
2.2. Categorization
3. Results
3.1. Adapting Model Based Approaches
3.1.1. Adapting Traditional Filtering Strategies
- a.
- Coupling collaborative filtering with machine learning algorithms:
- b.
- Coupling Collaborative filtering with other algorithms:
3.1.2. Adapting Matrix Factorization Model
- a.
- Coupling Matrix Factorization with machine learning algorithms:
- b.
- Coupling Matrix factorization with non-machine learning algorithms:
3.2. Adapting Memory Based Approaches
- a.
- Tags-based elicitation approach:
- b.
- Cold start recommendation in mobile environment:
3.3. Building Social Network Profile for Cold Start Users
Forming Users Social Circle
3.4. Others
3.5. Pictorial Summary of Findings
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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S/N | Articles | Platform, Data and Domain | Summary of Approaches |
---|---|---|---|
Coupling collaborative filtering with machine learning algorithms | |||
1 | [9] | Movielens (Movies) | Collaborative filtering algorithms combined with naive bayes and C4.5 classification algorithms |
2 | [23] | Movielens (Movies) | Model based collaborative filtering by a context aware to elicit auxiliary data |
3 | [24] | Netflix, jetstar, Movielens (Movies) | User demographic information together with collaborative filtering to address cold start problem using a SCOAL (Simultaneous co-clustering and learning) algorithm |
4 | [25] | Not stated | Adapting collaborative filtering using explicit trusted user neighbor data |
5 | [26] | Netflix (Movies) | Collaborative filtering merged with machine learning algorithms-deep learning algorithm (deep neural network) SADE, timeSVD++ |
6 | [27] | Opinions | Collaborative filtering merged with K-nearest neighbors |
7 | [28] | Collaborative filtering with word2vec unsupervised machine learning algorithm | |
Coupling collaborative filtering with other approaches | |||
1 | [29] | Tancent Weibo (Video) | Collaborative filtering merged with a hybrid random walk (HRW) method |
2 | [30] | Tancent Weibo (Video) | Collaborative filtering merged with a social group-based algorithm |
3 | [31] | Ontological approach | Collaborative filtering adapted with an ontological approach |
4 | [32] | WSDream (Web services) | A collaborative filtering web service QoS prediction approach that utilizes geographical neighborhoods knowledge |
S/N | Articles | Platform, Data and Domain | Summary of Approaches |
---|---|---|---|
Coupling matrix factorization with machine learning algorithms | |||
1 | [40] | Not stated | Matrix factorization coupled with regression tree and linear regression supervised learning models |
2 | [41] | Movielens | Matrix factorization with Markov chain (Markov random field prior (mrf-MF)) |
3 | [42] | Social media | Matrix factorization with Markov chain |
4 | [43] | Music | Merging Markov chain with matrix factorization model |
5 | [44] | Siena weibo (e-commerce) | Cross site (cross domain). Matrix factorization with modified gradient boosting trees and recurrent neural networks |
6 | [45] | Not stated | Cluster-based matrix factorization model with K means clustering algorithm for cold start cross domain recommendation |
7 | [46] | Videos & DVDs, Books, and Music | Cross-domain recommendation mechanism based partial least squares regression analysis. PLSR coupled with matrix factorization |
8 | [47] | Not stated | Cross-domain and cross-system recommendations coupling deep neural network with matrix factorization models |
Coupling matrix factorization with other approaches | |||
1 | [12] | DBpedia | Adapting matrix factorization with open linked data (MF-LOD) |
2 | [48] | Not Stated | algorithmic framework based on matrix factorization |
3 | [49] | Social media (Twitter) | ISoNTRE—is a framework called intelligent Social Network Transformer into Recommendation Engine is combined with matrix factorization |
4 | [50] | Douban movie iMDB (Movies) | CHRS uses a two-step approach to integrate the complementary in both the target and source networks for cold start recommendation utilizing the rich information from these two paths |
5 | [51] | Social media platforms (e-commerce) | Cross-domain cold start recommendation based on mapping user feature based matrix factorization from social media and product features from e-commerce website |
6 | [52] | Yelp; movielens (Movies) | Developed KAMF that use social links between users for cold start recommendation |
7 | [53]. | Movielens (Movies) | Matrix factorization with a content |
8 | [54] | Tancient Weibo (Movie) | Neighborhood and matrix factorization (hybrid—memory based and model based) |
9 | [55] | Tencent Weibo (microblogs) | Adapted matrix factorization adapted by coupling social behavior and social trust data |
S/N | Articles | Platform, Data and Domain | Summary of Approaches |
---|---|---|---|
1 | [63] | Facebook (movies) | Proposed a similarity metric for grouping users of the same interest for improving cold start movie recommendation through the use of KNN and K Means algorithms |
2 | [64] | Movielens, Elo7 (Movies) | Tag recommendation methods in a cold start scenario through syntactic and neighbourhood-based attributes. |
3 | [65] | Youtube (videos) | Video recommendation based on visual tags; visual description of the videos is used for the automatic annotation of the tags using K-Nearest Neighbor algorithm. |
4 | [66] | Mobile app | Predicting the next app to be used which involve a technique based on a set of features representing the real-time spatiotemporal contexts sensed by the home screen app for user cold start |
5 | [67] | Mobile app | Provides cross domain cold start recommendation for mobile apps from a mobile application domain to a news domain. |
6 | [68] | Mobile app | A modified incremental k-nearest neighbors (IkNN) and K-clustering algorithm for predicting next app for cold start |
7 | [69] | Mobile app | Location features and user interests are transferred from app usage data for cold-start location recommendation, |
S/N | Articles | Platform, Data and Domain | Summary of Approaches |
---|---|---|---|
1 | [27] | Opinions | Collaborative filtering merged with k-nearest neighbors |
2 | [85] | DBLP, Ciao, Douban, Opinions (Movies, books, music) | The coefficient of Jaccard is adapted as an intrinsic feature of the social network. A customized ranking model of items are formed for generating recommendation to cold start user |
3 | [86] | Leveraging the use of social media (Twitter) data to create a behavioral profile and classify users based on their behavior. Decision tree classifier and random forest machine learning techniques | |
4 | [87] | Social networks | Geographical distance and social network correlation are leveraged on location based social network LBSNs for building cold start user profile |
5 | [88] | Facebook (music, books) | Proposed three approaches to mitigating, which are: personality-based active learning, personality-based matrix factorization and personality-based cross-domain recommendation |
6 | [89] | Not stated | Social network data is used with collaborative filtering to exploit community preferences to address cold start recommendation |
S/N | Articles | Platform, Data and Domain | Summary of Approaches |
---|---|---|---|
1 | [30] | Tancient weibo (video) | Social-group-based algorithm for video recommendation |
2 | [59] | Music | Inter domain cross recommendation of using representation of music taste and past music listening for user cold start recommendation |
3 | [91] | ERS | Cold start recommendation in online learning adaptive systems using machine learning (classification and regression trees) |
4 | [92] | (Movies (yahoo movies)) | Probabilistic approach incorporating deep neural network (DNN) |
5 | [93] | Not stated | Probabilistic approach |
6 | [94] | Facebook (CARS). Location based) | Rule mining and community-based knowledge for user context-aware recommender systems |
7 | [95] | Conversational recommender system (critique-based recommendation) | |
8 | [96] | Movielens (Movies) | hybrid model with machine learning algorithms to provide recommendation for crowd workers |
9 | [97] | Movie | Movie videos representation using gradient boosting tree |
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Abdullah, N.A.; Rasheed, R.A.; Nasir, M.H.N.M.; Rahman, M.M. Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey. Appl. Sci. 2021, 11, 9608. https://doi.org/10.3390/app11209608
Abdullah NA, Rasheed RA, Nasir MHNM, Rahman MM. Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey. Applied Sciences. 2021; 11(20):9608. https://doi.org/10.3390/app11209608
Chicago/Turabian StyleAbdullah, Nor Aniza, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md. Nasir, and Md Mujibur Rahman. 2021. "Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey" Applied Sciences 11, no. 20: 9608. https://doi.org/10.3390/app11209608
APA StyleAbdullah, N. A., Rasheed, R. A., Nasir, M. H. N. M., & Rahman, M. M. (2021). Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey. Applied Sciences, 11(20), 9608. https://doi.org/10.3390/app11209608