Expedia Hotel Recommendations Team Project - University ✓ Solved

Topic Expedia Hotel Recommendations Team Project. University

Topic Expedia Hotel Recommendations Team Project. University of North America DATA522 – Solving Big Data Problems – Data Analytics Winter 2019 Anthony Estoya Ziteng Wu Srikanth Meesala

Objective Create personalized hotel recommendations for every user for each destination.

Data: train.csv, test.csv, destinations.csv, sample_submission.csv.

Tools: R Studio and Python (Kaggle competition).

Data Analytics Concepts: Clustering, Data Relationships, Logistic Regression.

Steps of Doing the Analysis: Explore the train.csv data; prepare the data; select the model; prepare the algorithm; test results; conclusion. Source of Data: Expedia Hotel Recommendations (Kaggle competition). Abstract: This project will describe steps taken to solve the Kaggle problem, learn the site usage and Python/R language, and explain the mathematical formulas and concepts used.

Paper For Above Instructions

Introduction

The Expedia Hotel Recommendations team project is a classic application of big data analytics to a real-world personalization problem: predicting hotel preferences for individual users across multiple destinations. The objective is to deliver accurate, scalable recommendations that improve user engagement and conversion while balancing computational constraints typical of large-scale recommender systems. Foundational work in recommender systems demonstrates that collaborative filtering, matrix factorization, and hybrid approaches offer strong performance with interpretable results (Koren, Bell, & Volinsky, 2009). In this project, the team integrates traditional methods with modern machine learning techniques to address cold-start and data sparsity, while leveraging the Kaggle Expedia dataset as a concrete benchmark (Expedia Hotel Recommendations competition, 2017).

Data Description and Preparation

The data package comprises train.csv, test.csv, destinations.csv, and sample_submission.csv. The train set typically includes user identifiers, destination identifiers, and contextual features such as destination popularity, user history signals, and session-level attributes. The test set mirrors the train structure minus the target variable, while destinations.csv provides destination metadata. Sample_submission.csv illustrates the expected prediction format for Kaggle submission. Data preparation involves handling missing values, encoding categorical features (users, destinations, and potential interactions), and constructing user- and destination-level feature matrices. Proper data preparation is critical; many success stories in recommender systems begin with robust feature engineering, including user bias terms, destination popularity, and interaction features (Koren et al., 2009; Ricci et al., 2015).

Methodological Approach

The project employs a layered approach combining clustering, collaborative filtering, and regression-based models. Clustering is used to segment users into cohorts with similar preferences, enabling more tailored model settings and faster inference for different user groups. Collaborative filtering, particularly matrix factorization, is applied to uncover latent factors representing user tastes and destination attractiveness. This aligns with foundational work that demonstrates the effectiveness of matrix factorization for recommender systems (Koren et al., 2009). To capture nonlinear interactions and higher-order effects, factorization machines are considered, offering a compact way to model interactions between sparse features (Rendle, 2010). Neural network-based approaches, such as neural collaborative filtering, provide nonlinear modeling capabilities and can capture complex patterns in user-Destination interactions (He et al., 2017). A contextual-bandit perspective is also discussed to address exploration-exploitation trade-offs in dynamic recommendation settings (Li et al., 2010).

Feature Engineering and Model Selection

Feature engineering focuses on three domains: user features (e.g., historical interactions, session length, recency of activity), destination features (e.g., popularity, price tier, location attributes), and interaction features (e.g., user-destination interaction counts, time-of-day effects). Embedding-like representations for users and destinations can be learned through matrix factorization or neural approaches to capture latent affinities. In practice, a hybrid model often yields the best results: a baseline logistic regression for interpretability and speed, augmented by matrix factorization or neural components to capture latent structure. This aligns with industry practice and established surveys that advocate hybrid methods to improve cold-start performance and accuracy ( Burke, 2002; Adomavicius & Tuzhilin, 2005). In-text use of these models is supported by the literature: matrix factorization remains a foundational method, with enhancements from factorization machines and neural collaborative filtering (Koren et al., 2009; Rendle, 2010; He et al., 2017). In addition, contextual-bandit methods provide principled exploration strategies in production systems where user interests can shift over time (Li et al., 2010).

Modeling Pipeline and Evaluation

The modeling pipeline includes train-test splits, cross-validation, and careful evaluation in terms of ranking metrics such as NDCG, MAP, and precision@k, alongside classic regression metrics like RMSE for any rating-like targets. This mirrors best practices from recommender research, where evaluation should reflect the user experience and business objectives (Herlocker et al., 2004; Ricci et al., 2015). The pipeline emphasizes scalable training (e.g., stochastic gradient descent for matrix factorization or online updates for contextual models) and robust validation to mitigate overfitting. The Expedia dataset provides a realistic test bed for comparing baseline models (e.g., logistic regression with engineered features) against more advanced methods (e.g., matrix factorization and neural networks), with results interpreted in the context of hotel-level recommendations across destinations (Kaggle Expedia competition, 2017).

Results and Discussion

Expected outcomes include improved ranking quality for hotel recommendations across destinations, reflected in higher NDCG@K and MAP@K scores compared with baseline models. In practice, hybrid models that combine both linear learners and latent factor representations tend to achieve better performance than any single approach, particularly in cold-start scenarios where user-hotel interactions are sparse (Koren et al., 2009; He et al., 2017). Additional gains may arise from context-aware features such as seasonal trends, price tiers, and location-based signals, which help disambiguate user preferences when multiple properties are similarly rated. The contextual-bandit perspective also informs practical deployment by balancing exploration and exploitation, enabling the system to surface novel or underrepresented hotels to users with limited history (Li et al., 2010).

Challenges, Limitations, and Ethical Considerations

Key challenges include data sparsity for new users and destinations, feature drift over time, and the computational demands of large-scale factorization and neural models. Cold-start remains a persistent issue, necessitating robust content-based features and cross-domain transferability. Evaluation must guard against overfitting to historical data and ensure fairness across destinations with varying popularity. Ethical considerations involve transparency of recommendations, user privacy, and avoiding reinforcing demographic biases through biased data representations (Adomavicius & Tuzhilin, 2005).

Conclusion and Future Work

The Expedia Hotel Recommendations team project illustrates how a well-designed blend of clustering, matrix factorization, and neural modeling can generate personalized hotel suggestions for users across destinations. The approach aligns with established theory and practice in recommender systems, offering scalable solutions that can adapt to new data and evolving user preferences (Koren et al., 2009; He et al., 2017). Future work includes refining cold-start strategies with richer content features, exploring temporal dynamics to capture seasonality, and implementing online learning and A/B testing to validate improvements in live environments (Ricci et al., 2015; Li et al., 2010).

References

  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. IEEE Computer, 42(8), 30-37.
  • Rendle, S. (2010). Factorization machines. Proceedings of the 10th IEEE International Conference on Data Mining (ICDM).
  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. World Wide Web Conference (WWW).
  • Li, L., Chu, W., Langford, J., & Wang, X. (2010). A contextual-bandit approach to personalized news article recommendation. Proceedings of the 19th International World Wide Web Conference (WWW).
  • Ricci, F., Rokach, L., Shapira, B., & Kantor, P. (2015). Recommender Systems Handbook. Springer.
  • Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.
  • Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5-53.
  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
  • McAuley, J., & Leskovec, J. (2013). Hidden factors and hidden topics: Understanding rating and review data. RecSys 2013.
  • Kaggle (2017). Expedia Hotel Recommendations competition. Retrieved from https://www.kaggle.com/c/expedia-hotel-recommendations

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