Service Recommendation with High Accuracy and Diversity

Shengqi, Wu; Kou, Huaizhen; Lv, Chao; Huang, Wanli; Qi, Lianyong; Wang, Hao

doi:10.1155/2020/8822992

Cited by 8 publications

(7 citation statements)

References 33 publications

(38 reference statements)

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“…The main reason to use the TF-IDF method is that it is suitable for the problem of weight extraction. In addition, the TF-IDF method is simple and easy to calculate [28].…”

Section: Improved Tf-idf Based Methodsmentioning

confidence: 99%

“…TF-IDF is a statistical method, which is often used to evaluate the importance of a word to a file. The importance of a word is directly proportional to the number of times it appears in the file, but at the same time, it is inversely proportional to the frequency it appears in the file library [28]. Based on the principle of TF-IDF, an improved user similarity calculation method is proposed to reduce the weight of the impact of popular items on the user similarity.…”

Section: Improved Tf-idf Based Methodsmentioning

confidence: 99%

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Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

Cai

Tang

et al. 2021

Applied Sciences

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The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

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“…The main reason to use the TF-IDF method is that it is suitable for the problem of weight extraction. In addition, the TF-IDF method is simple and easy to calculate [28].…”

Section: Improved Tf-idf Based Methodsmentioning

confidence: 99%

Section: Improved Tf-idf Based Methodsmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

Cai

Tang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm [41,42,43] is used to evaluate the importance of words in a textual corpus. The importance is proportional to the number of times the words appear in the document and inversely proportional to the frequency of words appearing in the corpus.…”

Section: Term Frequency-inverse Document Frequencymentioning

confidence: 99%

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Chiny¹,

Chihab²,

Bencharef³

et al. 2021

IJACSA

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Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rulebased sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and F1 score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.

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“…According to the view of SDL, both suppliers and demanders can act as resource integrators [26]. In addition, other social and economic actors can also act as resource integrators.…”

Section: Value Cocreation (Vcc)mentioning

confidence: 99%

The Value Cocreation Influence Mechanism of Network Freight Transport Platform in IoT‐Based Environments: Under the Service‐Dominant Logic

Bai

et al. 2021

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) has brought many benefits to the development of industries, from manufacturing to services. In the context of IoT, the network freight transport platform is equivalent to an integrator, integrating the scattered resources such as carriers and shippers in the supply chain, providing a solution, improving the use efficiency of vehicles, effectively reducing logistics cost, and creating value for the freight transport platform and bilateral customers. The purpose of this research is to explain the influence mechanism of transport demand subjects’ participation in value cocreation (VCC) under the service-dominant logic (SDL). We proposed a conceptual framework and 10 hypotheses and used SEM to measure the direct and mediating relationship among SDL, transport demand subject (TDS) participation, relational embeddedness (RE), platform VCC, and TDS value in the online business environment. The findings show that (1) SDL, TDS participation, and RE do not directly affect the TDS value, but indirectly through platform VCC; (2) SDL affects platform VCC not only directly but also indirectly through TDS participation and RE; (3) TDS participation affects platform VCC not only directly but also indirectly through RE; (4) RE indirectly affects TDS value by affecting platform VCC; and (5) platform VCC plays a mediating role in SDL, TDS participation, RE, and TDS value. Hence, transport enterprises should pay attention to the innovation concept, guide TDS participation, jointly improve platform VCC, give play to the network effect of the freight transport platform, and achieve TDS value through the deepening of TDS relationship embedding.

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Service Recommendation with High Accuracy and Diversity

Cited by 8 publications

References 33 publications

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

The Value Cocreation Influence Mechanism of Network Freight Transport Platform in IoT‐Based Environments: Under the Service‐Dominant Logic

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