Recommender systems are essential for a satisfying online experience. Industry giants like Facebook, Amazon, Netflix, and YouTube invest significant amounts of revenue in developing high-quality, personalised recommendations and advertisements to enhance user engagement and satisfaction. Consumers benefit from these systems because they can find and purchase items that suit their individual preferences. Collaborative and content-based filtering recommender systems are both used to achieve this. This paper concentrates mainly on the content-based filtering process while also examining the collaborative filtering approach.
The importance of recommender systems cannot be overstated.
When faced with a vast array of choices, like selecting headphones or an ice cream flavour, it’s common to feel overwhelmed. Fortunately, recommendation systems can help simplify the process by filtering out options that don’t align with our personal preferences and past experiences. As we make more purchases, businesses gather additional information to refine their recommendations and personalize them to our exact requirements.
A potential drawback of this approach is that without any relevant prior data, the algorithm may not be able to provide valuable recommendations to new customers. To alleviate this issue, other tactics like asking the customer for their preferred type of content or suggesting items based on the customer’s location or age group may be implemented.
There are two categories of recommender systems:
- Collaborative filtering
- To filter information based on its content
Filtering with group input
Collaborative filtering recommender systems depend solely on a user’s past actions to provide recommendations, without considering all possible items or their unique attributes.
By utilising previous user behaviour, collaborative filtering can make more precise forecasts about how a user is likely to react to a specific item in the future. This is commonly represented as a user-item interaction matrix, whereby the rows correspond to users and the columns represent items. To provide recommendations tailored to the individual user, similar users are grouped together, and their combined actions are considered.
Collaborative filtering can be categorised into two types: memory-based and model-based. Memory-based collaborative filtering uses user rating patterns to make predictions about their preferences, whereas model-based filtering relies on a predictive model, such as a neural network or a Bayesian network, to generate forecasts.
Memory-based collaborative strategy
In the memory-based collaborative filtering approach, a mathematical calculation utilises the user-item interaction matrix to determine the most similar items and recommend other products. This technique does not incorporate any machine learning models.
Model-based cooperative approach
There is an assumption that interactions between items and users follow certain principles. To enhance the user’s experience, the model’s refined predictions can be used to rank items that have not yet been interacted with. This steers the user towards products that are considered the most compatible with their preferences.
To filter information based on its content
Recommender systems use content-based filtering, which utilises machine learning techniques, to predict and recommend similar items to the user. For feature-based product recommendations to be effective, a comprehensive set of product attributes and a range of user preferences must be established.
The recommender system creates a profile for each user based on their previous interactions, such as clicks, ratings and likes. As users consistently interact with the service, the accuracy and appropriateness of their future recommendations will increase.
To illustrate how a content-based recommender system works, let’s consider the example of recommending movies.
For instance, suppose a user has watched and enjoyed the first two out of four movies.
The third movie will be chosen over the fourth movie because it is more similar to the first two. Similarity criteria that can be used to assess two films include, among other things, their cast, director, genre and runtime.
To record an individual’s interactions and preferences with particular items, a utility matrix is employed. By organising the information gathered from their daily experiences, it is feasible to establish the user’s preferences. Each interaction is given a numerical value, or “degree of preference,” which shows the significance of that particular experience to the individual.
The utility matrix illustration below is not comprehensive since not all users engage with the content provided by the service. It’s worth noting that the recommender model employs this utility matrix to create its recommendations.
A user profile, which is a set of numerical representations, is used to capture an individual’s preferences and aversions. This profile is created by monitoring the user’s previous actions and preferences, such as star ratings, clicks, likes, and dislikes. By using this information, a recommender system can more precisely determine the likelihood of the user reacting positively to future recommendations.
For content-based filtering to work effectively, it is vital that each item’s unique attributes accurately reflect its core values. To demonstrate this concept using movies as an example, a recommender system would need data such as the cast’s names, director, year of release, genre, and IMDb rating.
Content-based filtering often employs two of the most popular methods: cosine similarity and classification approach.
Preference is determined by computing the cosine similarity between the user’s vector and the item’s vector. For example, let’s say our hypothetical ideal customer prefers action films to thrillers and horror movies. In this situation, the vector related to action movies would have positive values, whereas the vector associated with horror movies would have negative values.
To illustrate, take a recently released science fiction action movie. If the cosine angle between the film vector and the user vector is relatively small, it indicates that the user would probably enjoy the movie, given their preference for action films. On the other hand, if the cosine similarity is large, we can disregard the movie as an unsuitable recommendation for the user.
A systematic categorisation approach
When making recommendations, it’s advisable to employ classification techniques like Bayesian classifiers or decision tree models. For instance, a decision tree model can be used to assist the user in filtering their options. At each level of the tree, the user’s selection can be refined to simplify the process of choosing an option.
The Pros and Cons of Content Filtering
- As personalised recommendations do not necessitate individual user information, it is practicable to offer services to a large number of clients. This is due to the scalability of the process, which allows for the management of a high volume of customers.
- Our recommendation system is customised to the unique preferences of each user, determined by their daily activities. This enables us to propose exceptionally targeted products that may captivate the user but might not appeal to the public at large.
- With the necessary infrastructure in place, we can make recommendations for new products soon after their release, without waiting for a census.
- As feature selection for content-based recommendation systems is often hard-coded, it’s crucial for developers to have a deep understanding of the field to build these systems effectively. Hence, the quality of the model is directly linked to the expertise and proficiency of the programmer.
- Depending on the user’s current preferences, the model might recommend other products, which could impede them from exploring and advancing to more advanced areas. This could limit their ability to interact with the product and its features, thereby restricting their entire experience.
- As the engine lacks sufficient information about a new user to generate recommendations, the cold start problem can be a major disadvantage.
- It’s difficult to suggest new content to inactive users.
A Comparison of Collaborative and Content-Based Filtering Recommender Systems
- While user interactions are not as crucial in content-based filtering, a considerable amount of data about the item’s features is still necessary. This data may include product dimensions, colour, brand, material, etc. It could also involve a movie’s cast, genre, director, year of release, etc.
- Although simple, collaborative filtering is a potent technique to suggest items that would appeal to an individual. It operates by clustering users with similar preferences and suggesting the intended user with items favoured by their group mates. This personalises the recommendations for the user, increasing the chances of them enjoying the suggested items optimally. For more information on similar techniques, click here.
- As content-based filtering manually incorporates item characteristics, the process relies heavily on specialised knowledge in the particular domain. Conversely, collaborative filtering does not require any domain experts’ input, as all the embeddings are learned automatically.
- Content-based methods necessitate both user and item data, while collaborative filtering systems require only user data.
In this article, we primarily discussed content-based filtering, a type of recommender system. We also briefly touched upon collaborative filtering, another type of recommender system. We learned that the content-based strategy employs two distinct methodologies to create recommendations: the classification model approach and the vector space approach, each with their unique benefits and drawbacks.
To streamline the process of suggesting new content and products to their target audience, businesses are resorting to recommender systems increasingly. In today’s online environment, these systems are crucial for successful business transactions. So, the next time you receive a recommendation on Facebook for something that could interest you, you’ll know the technology that powers it.