Recommender Systems are based on well-structured and incremental algorithms that have different strengths and weaknesses \cite{ricci2011introduction}. In general, the main existing RSs are concerned with improving accuracy with the premise of being useful to users. In this context, there is an increasing number of custom techniques that analyze the profile of the target user in order to better satisfy them \cite{bobadilla2013recommender}. In this sense, we conducted a broad study of the several metrics used in the literature to evaluate RSs. By organizational purposes, we divided these metrics into three groups: {\it Effectiveness-based}, {\it Complementary Dimensions of Quality} and {\it Domain Profiling}.\looseness=-1 \subsection{Effectiveness-based metrics} The main evaluation metrics are related to the accuracy, precision, and recall concepts \cite{herlocker2004evaluating}. Precision and recall are concepts that aim to quantify information …show more content…
We calculate serendipity by the complement of the cosine similarity between the items in the user's history and the items in a recommendation list \cite{zhang2012auralist}. Lower values indicate that recommendations deviate from a user's traditional behavior, and therefore bring greater surprise.\looseness=-1 \begin{equation} \label{seren} ser = 1 - \sum_{u \in S} \frac{1}{|S||H_u|} \sum_{h \in H_u} \sum_{i \in R_{u,20}} \frac{CosSim(i,h)}{20} \end{equation} \item \textbf{Catalog Coverage:} Catalog Coverage represents the fraction of relevant items that are recommended at least once, taking into consideration all the users. Larger catalog coverage indicate that the recommender balances the popularity bias by covering a large part of the set of items \cite{puthiya2016coverage}.\looseness=-1 \begin{equation} \label{catalogo} CC = \frac{| \bigcup_{u \in U}^{} R^{+}|}{|U|}
Based on the comparison Table 1, Table 2 and Table 3, we identified following set of categories on which we would like to evaluate the above tools and computing paradigm in subsequent sub-sections:
Bi et al \cite{Rec:Bi} provides ranked related entities to the user query along with the results of the main entity. In order to do this, this articles makes use of user's search history, click history and knowledge base. A matrix is created comprising of the user information which connects to the entities along with the ranking, click results. A tri-linear function\cite{Rec:Bi} is defined mapping these details and which will be used to rank the related entities
Now our service is to acquire the user who has the most likelihood to be recommended. First we use the probability function $P(r,u)$ using the communication between the user and recommender. If the probability is higher than our defined threshold value then grouped the user. All the nodes in this group are our expected nodes. Now for the nearest one we run BFS and extract the best one for recommendation.
σ_(R_X )=√(((∂R_X)/(∂V_1 ))^2 〖σ_(V_1 )〗^2+((∂R_X)/(∂V_2 ))^2 〖σ_(V_2 )〗^2+((∂R_X)/(∂I_D ))^2 〖σ_(I_D )〗^2+ ((∂R_X)/(∂R_S ))^2 〖σ_(R_S )〗^2 )
The overall score = sum of the product of weight and rating for each key dimension.
Support (c) = It is the number of occurrence of the item set in the consequent.
Any deviation identified during the evaluation is analyzed and classified into one of the three buckets:
ACCURACY – that the work is marked against the listed criteria. Evidence of a Candidate’s knowledge, understanding, skill or competence that can be used to make a judgment of their achievement against agreed standards/criteria
Objective performance measures tend to involve an unbiased measurement. Many of times this involves electronic timing devices, stopwatches, or distance measures. More so, in this instance the performance has a clear objective measure. However, within subjective performance measures it tends to be influenced by the observer's personal judgment of how the skill was performed. Thus, the abilities are closely scrutinized, observed, and criticized. This allows for more interpretation and opinion. These measures often refer to the quality and style of performance and are not always a clear and cut
Consistent with other United States examples, in the healthcare organization where I worked in the past, the unit’s performance metrics consisted of quantifiable indicators such
a comprehensive assessment system based on clearly defined performance measures. The system is used to
the entity it is applied to-that is, the object of the evaluation-may vary widely (Fitzpatrick, Sanders, & Worthen, 2010). Hopefully, all the information needed of a good evaluation is made available to me by the
Reliability - 137 – the extent of consistency, stability, and dependability of scores of the participants and/or rater. If using more than one grader, the graders should trained together and produce similar scores.
=> Add the reliability and validity of the measures with the article used on my review.
The Second assessment, it is assessed software via other main features, and then there are some tables that every software elaborated with more details. it is that point said these some evaluating in according to give rank which customers send them.