# Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey

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## Abstract

**:**

## 1. Introduction

## 2. Standard CF-Based Recommendation Techniques

#### 2.1. Typical Algorithms of CF

#### 2.1.1. Memory-Based CF

#### 2.1.2. Model-Based CF

#### 2.2. Evaluation Metrics of CF

#### 2.3. Main Issues and Challenges on Standard CF Techniques

#### 2.3.1. Data Sparsity

#### 2.3.2. Cold-Start

#### 2.3.3. Scalability

#### 2.3.4. Limitations of Numerical Explicit Ratings

## 3. User Review Texts

**Review Words**: The user review is in an unstructured textual form. The easiest way of mining it is to capture the most representative words. For instance, the TF-IDF weight measure [54] can be utilized to indicate the relevance of each word in the review. The extracted review words may be used to compute the similarity among users, rather than utilizing numerical ratings in CF [55].

**Review Topics**: The topics refer to an item’s aspects which a writer reviews in its review. For instance, in the review phrase: “The camera’s battery life is superb” the mentioned topics include the camera and its battery life. There are various methods for topics detection in reviews, namely, frequency-based, syntax-based, Conditional Random Fields [56], and topic modeling approaches like Latent Dirichlet Allocation (LDA) [57], Latent Semantic Analysis (LSA) [58], or Probabilistic Latent Semantic Analysis (PLSA) [59]. Review topics can then be used to improve real ratings in standard CF [34]. They can also be combined with latent factors in model-based CF [60] and with the similarity measure in neighbor-based CF [61].

**Overall Opinions**: They represent the sentiment orientation (i.e., positive or negative) of the user towards reviewed items. Generally, the overall opinion may be deducted by regrouping all opinion words’ sentiments in the reviews or by applying a coarse-grained sentiment analysis method based on supervised [62,63], semi-supervised [64] or unsupervised machine learning techniques [65]. The extracted overall opinions can be transformed into scalar ratings, that can be useful to augment CF techniques performance [66,67,68].

**Aspect Opinions**: They represent the detailed opinions about an item’ s particular characteristics. For example, the review phrase “The waiters’ attitude is great”. discloses a positive opinion on the service aspect. In general, review aspects may reference to a distinct thing like the product itself or one of its attributes (“attitude of waiters” rather than “service”). The typical techniques to feature extraction include linguistics-based methods and statistical methods [69,70,71,72], or structured models, like Conditional Random Fields (CRF) [73], Hidden Markov Models (HMM), and their variations [73,74]. The identification of opinions associated with aspects (features) is then made through word distance or pattern mining [69,75]. Alternatively, an SVM or LDA classifiers can be utilized for identifying the aspect opinions (aspect, sentiment pairs) [6]. In Reference [76], the aspect sentiments were utilized for calculating user similarities in order to cluster users in CF. In Reference [7] they were used to identify user similarities, and then incorporated into standard user-based CF.

## 4. CF Techniques Based on User Review Texts

#### 4.1. Techniques Based on Review Words

#### 4.2. Techniques Based on Review Topics

#### 4.3. Techniques Based on Review Sentiments

## 5. Practical Benefits of Review Incorporation

#### 5.1. Rating Sparsity

#### 5.2. Rating Prediction Improvement

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Table 1.**An example of a rating matrix [15].

User/Item | K-Pax | Life of Brian | Memento | Notorious |
---|---|---|---|---|

Alice | 4 | 3 | 2 | 4 |

Bob | ⌀ | 4 | 5 | 5 |

Cindy | 2 | 2 | 4 | ⌀ |

David | 3 | ⌀ | 5 | 2 |

Metrics | Definition | Formula | References |
---|---|---|---|

Mean Absolute Error | It measures the average of the absolute difference among the predicted ratings and true values. |
$$MAE=\frac{1}{\left|T\right|}\sum _{(u,i)\in T}|{\widehat{r}}_{u,i}-{r}_{u,i}|,$$
| [22] |

Root Mean Squared Error | It emphasizes the contributions of the absolute errors between the predictions and the real values. |
$$RMSE=\sqrt{\frac{1}{\left|T\right|}\sum _{(u,i)\in T}{({\widehat{r}}_{u,i}-{r}_{u,i})}^{2}}.$$
| [18] |

Precision | It computes the rate of the provided recommendations that are pertinent. |
$$Precision=\frac{|{U}_{u}\cap {L}_{rec}|}{{L}_{rec}},$$
| [18] |

Recall | It computes the rate of recommendations that are provided. |
$$Recall=\frac{|{U}_{u}\cap {L}_{rec}|}{{U}_{u}}.$$
| [18] |

ROC curve | It amplifies the proportion of recommendations that are not preferred by the user. | Plots the true positive rate against the false positive rate. | [18] |

Ranking Score | It measures the quality of recommendations based on their rank position. |
$$rank\left({L}_{rec}\right)=\sum _{j=1}^{|{L}_{rec}|}\frac{max({r}_{\left({i}_{j}\right)}-md,0)}{{2}^{\frac{j-1}{\alpha -1}}},$$
| [20] |

Click Trough Rate | It computes the proportion of recommendations ultimately clicked |
$$CTR=\frac{|{L}_{cons}|}{|{L}_{rec}|},$$
| [39,40] |

Novelty | It computes the novelty of the provided recommendations |
$$nov\left({L}_{rec}\right)=\sum _{i\in {L}_{rec}}mi{n}_{j\in {L}_{his}}dis(class\left(i\right),class\left(j\right)),$$
| [18,20] |

others | _ | _ | [18,20,22,36] |

Citation | User/Item Profile | Recommending Method | Tested Datasets | Main Contribution | Accuracy Performance | ||
---|---|---|---|---|---|---|---|

Product Reviews | Achieved Accuracy | Accuracy of CF Baselines | |||||

Terzi et al. [55] (Text–based user-kNN) | Review Words | User-based CF | Rottentomatoes (movies), Amazon (Audio CDs) | Improve accuracy (RMSE) | Audio CDs | 1.1092 | User-knn: 1.1190 Item-knn: 1.1130 SVD++ [82]: 1.1099 BMF [33]: 1.1105 |

Kim et al. [77] (ConvMF) | Latent ratings and item review words | CNN with Probabilistic Matrix Factorization (PMF) | Amazon (Instant Video), MovieLens (movies) | Enhance the rating prediction accuracy (RMSE) | Instant Video | 1.1337 | PMF [83]: 1.4118 CTR [84]: 1.5496 |

Zheng et al. [78] (DeepCoNN) | Latent factors from review words | CNN with Factorization Machine | Yelp (restaurants), Amazon (Musical instruments), Beer (beers) | Improve prediction accuracy (MSE), Alleviate the sparsity problem | Musical Instruments, restaurants and beers | 0.994 | MF [33]: 1.292 PMF [83]: 1.256 CTR [84]: 1.112 |

Chen et al. [79] (NARRE) | Latent factors from ratings, and latent factors based on reviews | CNN with MF | Amazon (Toys_and_Games, Kindle_Store, and Movies_and_TV), Yelp (businesses) | Increase prediction accuracy (RMSE), Interpretability in recommendations | Kindle Store | 0.7783 | PMF [83]: 0.9914 NMF [85]: 0.9023 SVD++ [82]: 0.7928 HFT [60]: 0.7917 DeepCoNN [78]: 0.7875 |

Wu et al. [80] (CARL) | Latent feature ratings, latent factors from review words | CNN and Factorization machine | Amazon (Musical Instruments, Office Products, Digital Music, Video Games, and Tools Improvement), RateBeer (Beer), Yelp (Restaurants) | Augment rating prediction performance (MSE) | Musical Instruments | 0.776 | PMF [83]: 1.401 ConvMF [77]: 0.991 DeepCoNN [78]: 0.814 RBLT [86]: 0.815 |

Liu et al. [81] (HRDR) | Explicit features from ratings, semantic features from reviews, ID embeddings | CNN with MF | Yelp 2013 and Yelp 2014 (yelp.com), Amazon (Video games and Gourmet food) | Augment recommendation accuracy (RMSE) | Video games | 1.011 | PMF [83]: 1.139 HFT [60]: 1.073 CTR [84]: 1.071 JMARS [87]: 1.064 ConvMF+[77]: 1.073 DeepCoNN [78]: 1.063 NARRE [79]: 1.055 |

Citation | User/Item Profile | Recommending Method | Tested Datasets | Main Contribution | Accuracy Performance | ||
---|---|---|---|---|---|---|---|

Product Reviews | Achieved Accuracy | Accuracy of CF Baselines | |||||

McAuley and Leskovec [60] (HFT) | Latent ratings merged with topic factors | Hidden Factors as Topics (HFT) | Amazon (movies, books, etc.), Beeradvocate and Ratebeer (wines, beers), Yelp (restaurants), etc. | Improves rating prediction accuracy (MAE), Tackle rating sparsity issue | 26 Amazon product categories | 1.329 | LFM [33]: 1.423 |

Tan et al. [86] (RBLT) | Latent topic opinions, latent rating factors | Matrix Factorization | Amazon (26 datasets [60]) | Prediction accuracy improvement (MSE), Alleviate data sparsity problem | Video Games | 1.462 | LFM [33]: 1.487 |

Bao et al. [88] (TopicMF) | Latent factors associated with topic factors | Topic Matrix factorization (TopicMF) | Amazon (arts, automotive, baby, beauty, etc.) [60]) | Enhance prediction accuracy (MSE) | 22 Amazon product categories | 1.3468 | PMF [83]: 1.5585 SVD++ [82]: 1.4393 |

Cheng et al. [89] (ALFM) | Latent topics, latent rating factors | Matrix Factorization | Amazon (26 datasets [60]), Yelp (businesses) | Improve prediction accuracy RMSE, Alleviate data sparsity problem, Interpretability in recommendations | Musical Instruments | 0.893 | BMF [33]: 1.004 |

Chin et al. [90] (ANR) | Latent aspect ratings and aspect importance | Aspect-based Neural Recommender | Amazon (24 datasets [60]), Yelp (businesses) | Prediction accuracy improvement (MSE) | Instant Video | 1.009 | DeepCoNN [78]: 1.178 ALFM [89]: 1.075 |

Citation | User/Item Profile | Recommending Method | Tested Datasets | Main Contribution | Accuracy Performance | ||
---|---|---|---|---|---|---|---|

Product Reviews | Achieved Accuracy | Accuracy of CF Baselines | |||||

Poirier et al. [66] | Ratings from opinion classification | Item-based CF | Flixster (movies) | Overcoming the cold-start issue (RMSE) | Movies | 0.898 | user-based CF: 0.897 |

Zhang et al. [91] (EFM) | Ratings and aspect sentiment scores | Factorization model | Yelp (businesses), Dianping (restaurants) | Improve prediction accuracy (RMSE) | Businesses | 1.212 | PMF [83]: 1.253 NMF [85]: 1.248 |

Diao et al. [87] (JMARS) | Latent ratings and aspects’ sentiment scores | Probabilistic matrix factorization | IMDB (movies) | Prediction accuracy increasing and address the cold start problem (MSE) | Movies | 4.97 | PMF [83]: 5.99 |

Ma et al. [7] (UPCF) | Ratings and aspects’ opinion ratings | User-based CF | Dianping (restaurants) | Accuracy increasing (RMSE), Deal with sparsity problem | Restaurants | 0.7707 | User-based CF: 0.7902 item- based CF: 0.8199 |

Musto et al. [92] (Multi-U2U) | Aspects’ opinion scores | Multi-criteria based user/item -based CF | Yelp (restaurants), TripAdvisor (hotels), Amazon (Video Games) | Increase prediction accuracy (MAE) | Video Games | 0.6276 | User-based CF: 0.9789 Item-based CF: 0.9679 |

Shen et al. [68] (SBFM) | Ratings with Reviews’ sentiment scores | Probabilistic matrix factorization | Amazon (Patio_lawn_ and_garden, Office products, Amazon instant video, Baby, Tools and home improvement, Beauty, Cellphones and accessories, Clothing and accessories) | Prediction accuracy improvement (Normalized RMSE) | Beauty | 0.2898 | MF [33]: 0.3411 PMF [83]: 0.3338 HFT [60]: 0.3085 |

Da’u et al. [93] (AODR) | Ratings and aspect-sentiment scores | Tensor Factorization | Amazon (Musical Instruments, Automotive, Instant Video), Yelp (businesses) | Augment Rating prediction and address data sparseness (RMSE, MAE) | Instant Video | 0.7990 | MF [33]: 0.9583 HFT [60]: 0.8172 RBLT [86]: 0.8061 |

Category | Approach | Characteristics | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

External NLP Tool Is Required | Consider Texts of Review as Simple Bag of Words | Static and Independent Vectors of Users and/or Items | Integrate Ratings in the Modeling Process of Reviews | Correlation between the Review’s Features | Emphasize the Pertinent Reviews or Parts of the Reviews | One-to-One Mapping (Latent Ratings and Latent Features) | Uses User-Specific Opinions on Item’s Features | Less Explainable and Informative | Powerful Representation Learning Abilities | Complex Implementation Process | ||

Word-based | Text–based user-kNN [55] | • | • | • | ||||||||

ConvMF [77] | • | • | • | • | ||||||||

DeepCoNN [78] | • | • | • | • | ||||||||

NARRE [79] | • | • | • | • | • | |||||||

CARL [80] | • | • | • | • | ||||||||

HRDR [81] | • | • | • | • | • | • | • | |||||

Topic-based | HFT [60] | • | • | • | ||||||||

RBLT [86] | • | • | • | • | ||||||||

TopicMF [88] | • | • | • | |||||||||

ALFM [89] | • | |||||||||||

ANR [90] | • | • | • | • | ||||||||

Sentiment-based | Poirier et al. [66] | • | • | • | • | |||||||

EFM [91] | • | • | • | • | ||||||||

JMARS [87] | • | • | • | • | ||||||||

UPCF [7] | • | • | • | • | ||||||||

Multi-U2U [92] | • | • | • | • | ||||||||

SBFM [68] | • | • | • | |||||||||

AODR [93] | • | • | • | • | • | • | • | • |

Approach | Condition on Datasets (Level of Sparsity) | Improvement in Accuracy Compared to Baselines |
---|---|---|

UPCF [7] | Dianping (Data-5) with #Reviews of each user: 5–9 | MSE of HFT [60] < MSE of User-based CF |

HFT [60] | Amazon (movies) with #Reviews for each user/product: 1–10 | MSE of LFM [33] − MSE of HFT > 0 |

RBLT [86] | Amazon (26 datasets) with #Reviews for each user/item: 1–10 | MSE of (LFM [33]/HFT [60]) − MSE of RBLT > 0 |

JMARS [87] | IMDB #training reviews for each user/movie: 1–100 | MSE of HFT [60] − MSE of JMARS > 0 |

ALFM [89] | Amazon (24 item categories) #reviews for each user/item: 1–10 | RMSE of (BMF [33]/HFT [60]/RBLT [86]) − RMSE of ALFM > 0 |

ConvMF [77] | MovieLens-1m: 7 sub-datasets of different densities (0.93%; 1.39%; 1.86%; 2.32%; 2.78%; 3.25%; 3.71%) | RMSE of ConvMF < RMSE of (PMF [83]/CTR [84]) |

DeepCoNN [78] | Three datasets: Yelp, Beer, and Amazon (Music Instruments) with #training reviews for each user/item: 1–5 | MSE of MF [33] − MSE of DeepConn > 0 |

AODR [93] | Amazon (Musical Instruments, Automotive, Instant Video) and Yelp datasets with #reviews for each user/item: 1–10 | RMSE of (BMF [33]/HFT [60]/RBLT [86]) − RMSE of AODR >0 MAE of (BMF [33]/HFT [60]/RBLT [86]) − MAE of AODR > 0 |

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## Share and Cite

**MDPI and ACS Style**

Srifi, M.; Oussous, A.; Ait Lahcen, A.; Mouline, S.
Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey. *Information* **2020**, *11*, 317.
https://doi.org/10.3390/info11060317

**AMA Style**

Srifi M, Oussous A, Ait Lahcen A, Mouline S.
Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey. *Information*. 2020; 11(6):317.
https://doi.org/10.3390/info11060317

**Chicago/Turabian Style**

Srifi, Mehdi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline.
2020. "Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey" *Information* 11, no. 6: 317.
https://doi.org/10.3390/info11060317