A Framework for Collaborative, Content-Based and Demographic Filtering

By Michael J. Pazzani. 1999

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “Demographic-based recommendation (Pazzani, 1999) is one of the earliest approaches to personalized recommendation systems.”
An energy-efficient mobile recommender system (2010, background): “Finally, a third way is to combine the above and have a hybrid solution [16].”
A unified approach to building hybrid recommender systems (2009, background): “” Other approaches include combining the results of different recommenders by weighting the resulting scores [8] or voting [23], switching between different recommenders [4], and filtering or reranking the results of one recommender with another [6].”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Several hybrid recommender systems, including Fab [8] and the “collaboration via content” approach, described in [76], are based on traditional collaborative techniques but also maintain the content-based profiles for each user.”
A recommender system using GA K-means clustering in an online shopping market (2008, background): “Moreover, it can recommend items in novel territories because it determines items to be recommended by considering only similarity between users, not characteristics of products (Konstan et al., 1997; Pazzani, 1999).”
Collaborative Filtering beyond the User-Item Matrix (2014, background): “Similarly, one can treat each item or each rating as a classification problem [Basilico and Hofmann 2004; Pazzani 1999] by taking all available ratings and/or side information of users and items as features of training instances.”
Collaborative Variational Autoencoder for Recommender Systems (2017, methodology): “Loosely coupled methods include the heuristic methods that implement separate collaborative and content-based systems and then combine the outputs obtained from individual recommender systems into nal recommendations using a linear combination [6] or a voting scheme [18].”
Recommender systems survey (2013, methodology): “The kinds of filtering most used at the beginning of the RS (collaborative , content-based and demographic) were described in [177].”
Personalized recommendation on dynamic content using predictive bilinear models (2009, background): “[34], aim to segment users based on personal attributes and make recommendations according to demographic classes.”
Hybrid Recommender Systems: Survey and Experiments (2002, methodology): “The aforementioned previous method includes the Soboroff method [10] that solved the sparse rating problem, the Fab method that solved the ﬁrst-rater problem, and the Pazzani method [6] that solved both the sparse rating problem and the ﬁrst-rater problem.”
Recommender Systems (2016, unknown): “Demographic recommender systems are discussed in [320, 475, 508], whereas utility-based recommender systems are discussed in [239].”
Designing and Evaluating Explanations for Recommender Systems (2011, background): “[6, 34, 48], we could only find one which offers an explanation facility: “For children it”
Exploring demographic information in social media for product recommendation (2015, background): “Typically, many existing studies utilize the demographic information obtained directly from user Web sites [3,45] or questionnaires [38].”
Explaining Recommendations: Design and Evaluation (2015, background): “[4, 39, 57], we could only find one which offers an explanation facility: “For”
A Taxonomy of Recommender Agents on the Internet (2003, background): “Several papers have attested to the high performance of hybrid systems (e.g., Pazzani 1999; Good et al. 1999).”
A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines (2013, background): “Another possibility for extension is to incorporate various content-based features such as user's demographic information, item's categorizations and others (Pazzani, 1999; Melville et al., 2002).”