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Sort of Recommender Systems

1. Collaborative filtering methods 
1. Memory-based methods
1. User-based collaborative filtering
2. Item-based collaborative filtering
2. Model-based methods
2. Content-based recommender methods 
3. Knowledge-based recommender systems
4. Hybrid systems

All of them are based on:

• Agent interactions – dynamic or time oriented
• Item attributes – static or features oriented
• Ratings matrices

Recommender Systems

Recommender systems are, after all, utilized by merchants to increase their profit. In order to achieve the broader business-centric goal of increasing revenue, the common operational and technical goals of recommender systems are as follows:

1. Relevance: The most obvious operational goal of a recommender system is to recommend items that are relevant to the user at hand. Users are more likely to consume items they find interesting. Although relevance is the primary operational goal of a recommender system, it is not sufficient in isolation. Therefore, we discuss several secondary goals below, which are not quite as important as relevance but are nevertheless important enough to have a significant impact.
2. Novelty: Recommender systems are truly helpful when the recommended item is something that the user has not seen in the past. For example, popular movies of a preferred genre would rarely be novel to the user. Repeated recommendation of popular items can also lead to reduction in sales diversity.
3. Serendipity: A related notion is that of serendipity [229], wherein the items recommended are somewhat unexpected, and therefore there is a modest element of lucky discovery, as opposed to obvious recommendations. Serendipity is different from novelty in that the recommendations are truly surprising to the user, rather than simply something they did not know about before. It may often be the case that a particular user may only be consuming items of a specific type, although a latent interest in items of other types may exist which the user might themselves find surprising. Unlike novelty, serendipitous methods focus on discovering such recommendations. For example, if a new Indian restaurant opens in a neighbourhood, then the recommendation of that restaurant to a user who normally eats Indian food is novel but not necessarily serendipitous. On the other hand, when the same user is recommended Ethiopian food, and it was unknown to the user that such food might appeal to her, then the recommendation is serendipitous. Serendipity has the beneficial side effect of increasing sales diversity or beginning a new trend of interest in the user. Increasing serendipity often has long-term and strategic benefits to the merchant because of the possibility of discovering entirely new areas of interest. On the other hand, algorithms that provide serendipitous recommendations often tend to recommend irrelevant items. In many cases, the longer term and strategic benefits of serendipitous methods outweigh these short-term disadvantages.
4. Increasing recommendation diversity: Recommender systems typically suggest a list of top-k items. When all these recommended items are very similar, it increases the risk that the user might not like any of these items. On the other hand, when the recommended list contains items of different types, there is a greater chance that the user might like at least one of these items. Diversity has the benefit of ensuring that the user does not get bored by repeated recommendation of similar items.

Loneliness

Process mining as the bridge between data science and process science

Process science is an umbrella term for the broader discipline that combines knowledge from information technology and knowledge from management sciences to improve and run operational processes

The ingredients contributing to data science

Data science

Data science is an interdisciplinary field aiming to turn data into real value. Data may be structured or unstructured, big or small, static or streaming. Value may be provided in the form of predictions, automated decisions, mod- els learned from data, or any type of data visualization delivering insights. Data science includes data extraction, data preparation, data exploration, data transformation, storage and retrieval, computing infrastructures, var- ious types of mining and learning, presentation of explanations and pre- dictions, and the exploitation of results taking into account ethical, social, legal, and business aspects.

The above definition implies that data science is broader than applied statistics and data mining. Data scientists assist organizations in turning data into value. A data scientist can answer a variety of data-driven questions. These can be grouped into the following four main categories:

• (Reporting) What happened?
• (Diagnosis) Why did it happen?
• (Prediction) What will happen?
• (Recommendation) What is the best that can happen?

An example of a customer journey illustrating the many (digital) touchpoints generating events that allow us to understand and serve customers better

Internet of Events

From Big Data to Smart Data, a closed loop

Universum - Urbi et Orbi

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Computer Science Spectre

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