Recommender systems require their recommendation algorithms to be accurate, scalable and should handle very sparse training data which keep changing over time. Inspired by ant colony optimization, we propose a novel collaborative filtering scheme: Ant Collaborative Filtering that enjoys those favorable characteristics above mentioned. With the mechanism of pheromone transmission between users and items, our method can pinpoint most relative users and items even in face of the sparsity problem. By virtue of the evaporation of existing pheromone, we capture the evolution of user preference over time. Meanwhile, the computation complexity is comparatively small and the incremental update can be done online. We design three experiments on three typical recommender systems, namely movie recommendation, book recommendation and music recommendation, which cover both explicit and implicit rating data. The results show that the proposed algorithm is well suited for real-world recommendation scenarios which have a high throughput and are time sensitive.
Model based recommendation algorithms include probabilistic methods and matrix factorization methods. The probabilistic methods assume there are some latent topics that both the users and the items belong to. The crux is to learn these probabilities that similar users and similar items are in similar topics. Probabilistic Latent Semantic Analysis (PLSA) @cite_8 is one of such algorithms. Another model-based algorithm, Non-negative Matrix Factorization (NMF) @cite_1 belongs to the matrix factorization methods that also have many extensions and applications in CF domain. By restricting the rank of the factorized matrices as the user profile and item profile, these methods are themselves the well known dimension reduction techniques and are considered to be the state-of-art of CF algorithms @cite_2 .
@cite_2 @cite_1 @cite_8
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Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset. We use a low-dimensional linear model to describe the user rating matrix in a recommendation system. A non-negativity constraint is enforced in the linear model to ensure that each user’s rating profile can be represented as an additive linear combination of canonical coordinates. In order to learn such a constrained linear model from an incomplete rating matrix, we introduce two variations on Non-negative Matrix Factorization (NMF): one based on the Expectation-Maximization (EM) procedure and the other a Weighted Nonnegative Matrix Factorization (WNMF). Based on our experiments, the EM procedure converges well empirically and is less susceptible to the initial starting conditions than WNMF, but the latter is much more computationally efficient. Taking into account the advantages of both algorithms, a hybrid approach is presented and shown to be effective in real data sets. Overall, the NMF-based algorithms obtain the best prediction performance compared with other popular collaborative filtering algorithms in our experiments; the resulting linear models also contain useful patterns and features corresponding to user communities. Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. We investigate several variations to deal with discrete and continuous response variables as well as with different objective functions. The main advantages of this technique over standard memory-based methods are higher accuracy, constant time prediction, and an explicit and compact model representation. The latter can also be used to mine for user communitites. The experimental evaluation shows that substantial improvements in accucracy over existing methods and published results can be obtained.